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1

Expert Panel: Forecast Future Demand for Medical Isotopes  

Broader source: Energy.gov (indexed) [DOE]

Expert Panel: Expert Panel: Forecast Future Demand for Medical Isotopes March 1999 Expert Panel: Forecast Future Demand for Medical Isotopes September 25-26, 1998 Arlington, Virginia The Expert Panel ............................................................................................. Page 1 Charge To The Expert Panel........................................................................... Page 2 Executive Summary......................................................................................... Page 3 Introduction ...................................................................................................... Page 4 Rationale.......................................................................................................... Page 6 Economic Analysis...........................................................................................

2

Expert Panel: Forecast Future Demand for Medical Isotopes | Department of  

Broader source: Energy.gov (indexed) [DOE]

Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and stimulate biomedical research. The expert panel recommends that the U.S. government build this capability around either a reactor, an accelerator or a combination of both technologies as long as isotopes for clinical and research applications can be supplied reliably, with diversity in adequate

3

China's Present Situation of Coal Consumption and Future Coal Demand Forecast  

Science Journals Connector (OSTI)

This article analyzes China's coal consumption changes since 1991 and proportion change of coal consumption to total energy consumption. It is argued that power, iron and steel, construction material, and chemical industries are the four major coal consumption industries, which account for 85% of total coal consumption in 2005. Considering energy consumption composition characteristics of these four industries, major coal demand determinants, potentials of future energy efficiency improvement, and structural changes, etc., this article makes a forecast of 2010s and 2020s domestic coal demand in these four industries. In addition, considering such relevant factors as our country's future economic growth rate and energy saving target, it forecasts future energy demands, using per unit GDP energy consumption method and energy elasticity coefficient method as well. Then it uses other institution's results about future primary energy demand, excluding primary coal demand, for reference, and forecasts coal demands in 2010 and 2020 indirectly. After results comparison between these two methods, it is believed that coal demands in 2010 might be 2620–2850 million tons and in 2020 might be 3090–3490 million tons, in which, coal used in power generation is still the driven force of coal demand growth.

Wang Yan; Li Jingwen

2008-01-01T23:59:59.000Z

4

Energy Demand Forecasting  

Science Journals Connector (OSTI)

This chapter presents alternative approaches used in forecasting energy demand and discusses their pros and cons. It... Chaps. 3 and 4 ...

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

5

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

Demand side energy management has become an important issue for energy management. In order to support energy planning and policy decisions forecasting the future demand is very important. Thus, forecasting the f...

?Irem Uçal Sar?; Ba¸sar Öztay¸si

2012-01-01T23:59:59.000Z

6

Future scenarios and trends in energy generation in brazil: supply and demand and mitigation forecasts  

Science Journals Connector (OSTI)

Abstract The structure of the Brazilian energy matrix defines Brazil as a global leader in power generation from renewable sources. In 2011, the share of renewable sources in electricity production reached 88.8%, mainly due to the large national water potential. Although the Brazilian energy model presents a strong potential for expansion, the total energy that could be used with most current renewable technologies often outweighs the national demand. The current composition of the national energy matrix has outstanding participation of hydropower, even though the country has great potential for the exploitation of other renewable energy sources such as wind, solar and biomass. This document therefore refers to the trend of evolution of the Brazilian Energy Matrix and exposes possible mitigation scenarios, also considering climate change. The methodology to be used in the modeling includes the implementation of the LEAP System (Long-range Energy Alternatives Planning) program, developed by the Stockholm Environment Institute, which allows us to propose different scenarios under the definition of socioeconomic scenarios and base power developed in the context of the REGSA project (Promoting Renewable Electricity Generation in South America). Results envision future scenarios and trends in power generation in Brazil, and the projected demand and supply of electricity for up to 2030.

José Baltazar Salgueirinho Osório De Andrade Guerra; Luciano Dutra; Norma Beatriz Camisão Schwinden; Suely Ferraz de Andrade

2014-01-01T23:59:59.000Z

7

Demand Forecasting of New Products  

E-Print Network [OSTI]

Keeping Unit or SKU) employing attribute analysis techniques. The objective of this thesis is to improve Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock

Sun, Yu

8

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

Accurate forecasting of energy demand plays a key role for utility companies, network operators, producers and suppliers of energy. Demand forecasts are utilized for unit commitment, market bidding, network operation and maintenance, integration of renewable ... Keywords: analytics, energy demand forecasting, machine learning, renewable energy sources, smart grids, smart meters

Mathieu Sinn

2014-06-01T23:59:59.000Z

9

Applying Bayesian Forecasting to Predict New Customers' Heating Oil Demand.  

E-Print Network [OSTI]

??This thesis presents a new forecasting technique that estimates energy demand by applying a Bayesian approach to forecasting. We introduce our Bayesian Heating Oil Forecaster… (more)

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

10

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand ........................................................................ 28 Possible Future Trends for Plug-in Hybrid Electric Vehicles .............................................................. 23 Electricity Demand Growth in the West

11

AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.  

E-Print Network [OSTI]

AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

Povinelli, Richard J.

12

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of demand.

Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

2005-07-01T23:59:59.000Z

13

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

This paper presents the National Energy Board’s long term energy demand forecasting model in its present state of ... results of recent research at the NEB. Energy demand forecasts developed with the aid of this....

R. A. Preece; L. B. Harsanyi; H. M. Webster

1980-01-01T23:59:59.000Z

14

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network [OSTI]

......................................................................... 11 3. Demand Side Management (DSM) Program Impacts................................... 13 4. Demand Sylvia Bender Manager DEMAND ANALYSIS OFFICE Scott W. Matthews Chief Deputy Director B.B. Blevins Forecast Methods and Models ....................................................... 14 5. Demand-Side

15

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

Logistics parks’ demand is an important basis of establishing the development policy of logistics industry and logistics infrastructure for planning. In order to improve the forecast accuracy of logistics parks’ demand, a combination forecasting ... Keywords: Logistics parks' demand, combine, simulated annealing algorithm, grey forecast model, exponential smoothing method

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

16

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data in California and for climate zones within those areas. The staff California Energy Demand 2008-2018 forecast

17

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse three decades of experience working with telecommunications operators around the world we seek

McBurney, Peter

18

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

resources resulting in water stress. Effective water management ­ a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting waterFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil

Keller, Arturo A.

19

Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands  

Science Journals Connector (OSTI)

A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and statistical forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.

Argon Chen; Jakey Blue

2010-01-01T23:59:59.000Z

20

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Univariate time-series forecasting of monthly peak demand of electricity in northern India  

Science Journals Connector (OSTI)

This study forecasts the monthly peak demand of electricity in the northern region of India using univariate time-series techniques namely Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) and Holt-Winters Multiplicative Exponential Smoothing (ES) for seasonally unadjusted monthly data spanning from April 2000 to February 2007. In-sample forecasting reveals that the MSARIMA model outperforms the ES model in terms of lower root mean square error, mean absolute error and mean absolute percent error criteria. It has been found that ARIMA (2, 0, 0) (0, 1, 1)12 is the best fitted model to explain the monthly peak demand of electricity, which has been used to forecast the monthly peak demand of electricity in northern India, 15 months ahead from February 2007. This will help Northern Regional Load Dispatch Centre to make necessary arrangements a priori to meet the future peak demand.

Sajal Ghosh

2008-01-01T23:59:59.000Z

22

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

Exponential smoothing methods are widely used as forecasting techniques in industry and business. Their usual formulation, however, does not allow covariates to be used for introducing extra information into the forecasting process. In this paper, we analyse an extension of the exponential smoothing formulation that allows the use of covariates and the joint estimation of all the unknowns in the model, which improves the forecasting results. The whole procedure is detailed with a real example on forecasting the daily demand for electricity in Spain. The time series of daily electricity demand contains two seasonal patterns: here the within-week seasonal cycle is modelled as usual in exponential smoothing, while the within-year cycle is modelled using covariates, specifically two harmonic explanatory variables. Calendar effects, such as national and local holidays and vacation periods, are also introduced using covariates. [Received 28 September 2010; Revised 6 March 2011, 2 October 2011; Accepted 16 October 2011

José D. Bermúdez

2013-01-01T23:59:59.000Z

23

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc, 2000 Abstract The marketing team of a new telecommunications company is usually tasked with producing involved in doing so. Based on our three decades of experience working with telecommunications operators

Parsons, Simon

24

NOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future)  

E-Print Network [OSTI]

conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through are generated every 6 hours and you can step backward in hourly increments to view conditions over the previousNOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future) and Nowcasts

25

Medium-term forecasting of demand prices on example of electricity prices for industry  

Science Journals Connector (OSTI)

In the paper, a method of forecasting demand prices for electric energy for the industry has been suggested. An algorithm of the forecast for 2006–2010 based on the data for 1997–2005 has been presented.

V. V. Kossov

2014-09-01T23:59:59.000Z

26

Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance  

Science Journals Connector (OSTI)

Modeling the lead-time demand for the multiple slow-moving inventory items in the case when the available requests history is very short is a challenge for inventory management. The classical forecasting technique, which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity, is known to lead to very poor performance in many cases important for industrial applications. An alternative approach to the demand forecasting for the considered problem is based on the Bayesian paradigm, when the initially developed population-averaged demand probability distribution is modified for each item using its specific requests history. This paper follows this approach and presents a new model, which relies on the beta distribution as a prior for the request probability, and allows to account for disparity in variance of demand between different stock keeping units. To estimate the model parameters, a special computationally effective technique based on the generalized method of moments is developed. Simulation results indicate the superiority of the proposed model over the known ones, while the computational burden does not increase.

Alexandre Dolgui; Maksim Pashkevich

2008-01-01T23:59:59.000Z

27

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future  

SciTech Connect (OSTI)

Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and to nearly 30.2 billion tons in 2050. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand, the trends and 2050 outlook for these factors, and their anticipated effect on freight demand. After describing federal policy actions that could influence future freight demand, the report then summarizes the capabilities of available analytical models for forecasting freight demand. This is one in a series of reports produced as a result of the Transportation Energy Futures project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for reducing GHGs and petroleum dependence related to transportation.

Grenzeback, L. R.; Brown, A.; Fischer, M. J.; Hutson, N.; Lamm, C. R.; Pei, Y. L.; Vimmerstedt, L.; Vyas, A. D.; Winebrake, J. J.

2013-03-01T23:59:59.000Z

28

Energy Demand Forecasting in China Based on Dynamic RBF Neural Network  

Science Journals Connector (OSTI)

A dynamic radial basis function (RBF) network model is proposed for energy demand forecasting in this paper. Firstly, we ... detail. At last, the data of total energy demand in China are analyzed and experimental...

Dongqing Zhang; Kaiping Ma; Yuexia Zhao

2011-01-01T23:59:59.000Z

29

Review/Verify Strategic Skills Needs/Forecasts/Future Mission...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

ReviewVerify Strategic Skills NeedsForecastsFuture Mission Shifts Annual Lab Plan (1-10 yrs) Fermilab Strategic Agenda (2-5 yrs) Sector program Execution Plans (1-3...

30

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network [OSTI]

Forecasts Using NEMS and GIS National Climatic Data Center.with Changing Boundaries." Use of GIS to Understand Socio-Forecasts Using NEMS and GIS Appendix A. Map Results Gallery

Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

2005-01-01T23:59:59.000Z

31

Enhanced Oil Recovery to Fuel Future Oil Demands | GE Global...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

to Fuel Future Oil Demands Enhanced Oil Recovery to Fuel Future Oil Demands Trevor Kirsten 2013.10.02 I'm Trevor Kirsten and I lead a team of GE researchers that investigate a...

32

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect (OSTI)

The history of the petrochemical industry over the past ten years clearly shows that forecasting in a turbulent world is like trying to predict tomorrow's headlines.

Struth, B.W.

1984-08-01T23:59:59.000Z

33

Univariate forecasting of day-ahead hourly electricity demand in the northern grid of India  

Science Journals Connector (OSTI)

Short-term electricity demand forecasts (minutes to several hours ahead) have become increasingly important since the rise of the competitive energy markets. The issue is particularly important for India as it has recently set up a power exchange (PX), which has been operating on day-ahead hourly basis. In this study, an attempt has been made to forecast day-ahead hourly demand of electricity in the northern grid of India using univariate time-series forecasting techniques namely multiplicative seasonal ARIMA and Holt-Winters multiplicative exponential smoothing (ES). In-sample forecasts reveal that ARIMA models, except in one case, outperform ES models in terms of lower RMSE, MAE and MAPE criteria. We may conclude that linear time-series models works well to explain day-ahead hourly demand forecasts in the northern grid of India. The findings of the study will immensely help the players in the upcoming power market in India.

Sajal Ghosh

2009-01-01T23:59:59.000Z

34

A Transaction Choice Model for Forecasting Demand for Alternative-Fuel Vehicles  

E-Print Network [OSTI]

Forecasting Demand Alternative-Fuel Vehicles for DavldNG DEMANDFOR ALTERNATIVE-FUEL VEHICLES DavidBrownstone,interested in promoting alternative-fuel vehicles. Tl’us is

Brownstone, David; Bunch, David S.; Golob, Thomas F.; Ren, Weiping

1996-01-01T23:59:59.000Z

35

A Transactions Choice Model for Forecasting Demand for Alternative-Fuel Vehicles  

E-Print Network [OSTI]

Forecasting Demand Alternative-Fuel Vehicles for DavldNG DEMANDFOR ALTERNATIVE-FUEL VEHICLES DavidBrownstone,interested in promoting alternative-fuel vehicles. Tl’us is

Brownstone, David; Bunch, David S; Golob, Thomas F; Ren, Weiping

1996-01-01T23:59:59.000Z

36

Forecasting intermittent demand by hyperbolic-exponential smoothing  

Science Journals Connector (OSTI)

Abstract Croston’s method is generally viewed as being superior to exponential smoothing when the demand is intermittent, but it has the drawbacks of bias and an inability to deal with obsolescence, where the demand for an item ceases altogether. Several variants have been reported, some of which are unbiased on certain types of demand, but only one recent variant addresses the problem of obsolescence. We describe a new hybrid of Croston’s method and Bayesian inference called Hyperbolic-Exponential Smoothing, which is unbiased on non-intermittent and stochastic intermittent demand, decays hyperbolically when obsolescence occurs, and performs well in experiments.

S.D. Prestwich; S.A. Tarim; R. Rossi; B. Hnich

2014-01-01T23:59:59.000Z

37

Bayesian forecasting of demand time-series data with zero values  

Science Journals Connector (OSTI)

This paper describes the development of a Bayesian procedure to analyse and forecast positive demand time-series data with a proportion of zero values and a high level of variability for the non-zero data. The resulting forecasts play decisive roles in organisational planning, budgeting, and performance monitoring. Exponential smoothing methods are widely used as forecasting techniques in industry and business. However, they can be unsuitable for the analysis of non-negative demand time-series data with the aforementioned features. In this paper, an unconstrained latent demand underlying the observed demand is introduced into the linear heteroscedastic model associated with the Holt-Winters model. Accurate forecasts for the observed demand can readily be derived from those obtained with exponential smoothing for the latent demand. The performance of the proposed procedure is illustrated using a simulation study and two real time-series datasets which correspond to tourism demand and book sales. [Received 4 November 2010; Revised 7 September 2011, 10 April 2012; Accepted 10 May 2012

Ana Corberán-Vallet; José D. Bermúdez; Enriqueta Vercher

2013-01-01T23:59:59.000Z

38

Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables  

Science Journals Connector (OSTI)

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios were developed by either assigning arbitrary values or assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and inputted to the scenario set. This article focuses on the long-term forecasting of electricity demand using autoregressive, simple linear and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario's electricity demand as a case study, the annual energy, peak load and base load demand were forecasted up to the year 2025. In order to generate different scenarios, different ranges in the economic, demographic and climatic variables were used. [Received 16 October 2007; Revised 31 May 2008; Revised 25 October 2008; Accepted 1 November 2008

F. Chui; A. Elkamel; R. Surit; E. Croiset; P.L. Douglas

2009-01-01T23:59:59.000Z

39

Incorporating heterogeneity to forecast the demand of new products in emerging markets: Green cars in China  

Science Journals Connector (OSTI)

Abstract Emerging markets are becoming increasingly important for many companies and it is not surprising to see that an increasing number of new products, especially technology products, are now being launched in these markets fairly quickly after they are launched in Western markets. However, most of the research on forecasting demand for new products focuses on developed markets. Marketing managers in multinational companies may therefore be tempted to use models that have been applied in developed markets to forecast demand of new products in emerging markets. However, there is ample evidence that supports the contention that emerging markets are different to markets in developed economies. This research proposes a dynamic segmentation approach to forecast demand that explicitly incorporates heterogeneity of consumers within and across segments: a key distinguishing feature of emerging markets. The research is applied in the context of the Chinese green car market but can be replicated for other products and in similar market conditions.

Lixian Qian; Didier Soopramanien

2014-01-01T23:59:59.000Z

40

Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030  

Science Journals Connector (OSTI)

Natural gas is the primary source for electricity production in Turkey. However, Turkey does not have indigenous resources and imports more than 98.0% of the natural gas it consumes. In 2011, more than 20.0% of Turkey's annual trade deficit was due to imported natural gas, estimated at US$ 20.0 billion. Turkish government has very ambitious targets for the country's energy sector in the next decade according to the Vision 2023 agenda. Previously, we have estimated that Turkey's annual electricity demand would be 530,000 GWh at the year 2023. Considering current energy market dynamics it is almost evident that a substantial amount of this demand would be supplied from natural gas. However, meticulous analysis of the Vision 2023 goals clearly showed that the information about the natural gas sector is scarce. Most importantly there is no demand forecast for natural gas in the Vision 2023 agenda. Therefore, in this study the aim was to generate accurate forecasts for Turkey's natural gas demand between 2013 and 2030. For this purpose, two semi-empirical models based on econometrics, gross domestic product (GDP) at purchasing power parity (PPP) per capita, and demographics, population change, were developed. The logistic equation, which can be used for long term natural gas demand forecasting, and the linear equation, which can be used for medium term demand forecasting, fitted to the timeline series almost seamlessly. In addition, these two models provided reasonable fits according to the mean absolute percentage error, MAPE %, criteria. Turkey's natural gas demand at the year 2030 was calculated as 76.8 billion m3 using the linear model and 83.8 billion m3 based on the logistic model. Consequently, found to be in better agreement with the official Turkish petroleum pipeline corporation (BOTAS) forecast, 76.4 billion m3, than results published in the literature.

Mehmet Melikoglu

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Supply/Demand Forecasts Begin to Show Stock Rebuilding  

Gasoline and Diesel Fuel Update (EIA)

9 9 Notes: During 1999, we saw stock draws during the summer months, when we normally see stock builds, and very large stock draws during the winter of 1999/2000. Normally, crude oil production exceeds product demand in the spring and summer, and stocks build. These stocks are subsequently drawn down during the fourth and first quarters (dark blue areas). When the market is in balance, the stock builds equal the draws. During 2000, stocks have gradually built, but following the large stock draws of 1999, inventories needed to have been built more to get back to normal levels. As we look ahead using EIA's base case assumptions for OPEC production, non-OPEC production, and demand, we expect a more seasonal pattern for the next 3 quarters. But since we are beginning the year with

42

World Energy Demand  

Science Journals Connector (OSTI)

A reliable forecast of energy resources, energy consumption, and population in the future is a ... So, instead of absolute figures about future energy demand and sources worldwide, which would become...3.1 correl...

Giovanni Petrecca

2014-01-01T23:59:59.000Z

43

Analysis of PG&E`s residential end-use metered data to improve electricity demand forecasts -- final report  

SciTech Connect (OSTI)

This report summarizes findings from a unique project to improve the end-use electricity load shape and peak demand forecasts made by the Pacific Gas and Electric Company (PG&E) and the California Energy Commission (CEC). First, the direct incorporation of end-use metered data into electricity demand forecasting models is a new approach that has only been made possible by recent end-use metering projects. Second, and perhaps more importantly, the joint-sponsorship of this analysis has led to the development of consistent sets of forecasting model inputs. That is, the ability to use a common data base and similar data treatment conventions for some of the forecasting inputs frees forecasters to concentrate on those differences (between their competing forecasts) that stem from real differences of opinion, rather than differences that can be readily resolved with better data. The focus of the analysis is residential space cooling, which represents a large and growing demand in the PG&E service territory. Using five years of end-use metered, central air conditioner data collected by PG&E from over 300 residences, we developed consistent sets of new inputs for both PG&E`s and CEC`s end-use load shape forecasting models. We compared the performance of the new inputs both to the inputs previously used by PG&E and CEC, and to a second set of new inputs developed to take advantage of a recently added modeling option to the forecasting model. The testing criteria included ability to forecast total daily energy use, daily peak demand, and demand at 4 P.M. (the most frequent hour of PG&E`s system peak demand). We also tested the new inputs with the weather data used by PG&E and CEC in preparing their forecasts.

Eto, J.H.; Moezzi, M.M.

1993-12-01T23:59:59.000Z

44

Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)  

Reports and Publications (EIA)

The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

1998-01-01T23:59:59.000Z

45

An evaluation of forecasting methods for aircraft non-routine maintenance material demand  

Science Journals Connector (OSTI)

Aircraft maintenance can be divided into routine and non-routine activities. Material demand associated with non-routine maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-routine maintenance material demand. Non-routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-routine maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-routine maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate maintenance C-check.

Maarten Zorgdrager; Wim J.C. Verhagen; Richard Curran

2014-01-01T23:59:59.000Z

46

Modelling future private car energy demand in Ireland  

Science Journals Connector (OSTI)

Targeted measures influencing vehicle technology are increasingly a tool of energy policy makers within the EU as a means of meeting energy efficiency, renewable energy, climate change and energy security goals. This paper develops the modelling capacity for analysing and evaluating such legislation, with a focus on private car energy demand. We populate a baseline car stock and car activity model for Ireland to 2025 using historical car stock data. The model takes account of the lifetime survival profile of different car types, the trends in vehicle activity over the fleet and the fuel price and income elasticities of new car sales and total fleet activity. The impacts of many policy alternatives may only be simulated by such a bottom-up approach, which can aid policy development and evaluation. The level of detail achieved provides specific insights into the technological drivers of energy consumption, thus aiding planning for meeting climate targets. This paper focuses on the methodology and baseline scenario. Baseline results for Ireland forecast a decline in private car energy demand growth (0.2%, compared with 4% in the period 2000–2008), caused by the relative growth in fleet efficiency compared with activity.

Hannah E. Daly; Brian P. Ó Gallachóir

2011-01-01T23:59:59.000Z

47

Future Sustainability Forecasting by Exchange Markets: Basic Theory and an Application  

Science Journals Connector (OSTI)

Future Sustainability Forecasting by Exchange Markets: Basic Theory and an Application ... For example, there are often subtle but persistent price signals embedded in long-term investment decisions and stock price fluctuations. ...

Nataliya Malyshkina; Deb Niemeier

2010-11-08T23:59:59.000Z

48

Forecast of U. S. Refinery Demand for NGL's (natural gas liquids) in 1978-1985  

SciTech Connect (OSTI)

A forecast of U.S. Refinery Demand for NGL's (Natural Gas Liquids) in 1978-1985 is based on a predicted 1.4%/yr decline in motor gasoline consumption from 7.4 to 6.7 million bbl/day (Mbd), including a 2.6%/yr reduction from 5.3 to 4.4 Mbd for automobiles and a 1.3%/yr growth from 2.1 to 2.3 Mbd for trucks, because of slow growth rates in the U.S. automobile fleet (1.1%/yr) and average annual miles driven (0.9%/yr), a 3.9%/yr growth in average mileage from 14.2 to 18.6 mpg, and diesel penetration to the automobile market which should increase from 0.3 to 3.3%. Leaded gasoline's share is expected to decline from 68% of the market (5.1 Mbd, including 0.8 Mbd leaded premium) to 24% (1.7 Mbd, leaded regular only), including a drop from 56 to 6% for automobiles and from approx. 100 to 60% for trucks. This will require increased production of clean-octane reformates and alkylates and reduce the need for straight-run gasolines, but because of the decline in the total gasoline demand, these changes should be minimal. Butane demand from outside-refinery production should decrease by 5-6%/yr, and natural gasoline will be consumed according to available production as an isopentane source.

Laskosky, J.

1980-01-01T23:59:59.000Z

49

A Monte Carlo approach to forecasting the demand for offshore supply vessels  

Science Journals Connector (OSTI)

In the near future, the demand for offshore supply vessels in Brazil will be driven by the activities induced by the bids carried out by the regulatory agency, ANP. The likely tendency is to increase the number of bids and consequently, the demand for vessels in the coming years. The proposed model consists of a Monte Carlo simulation of the offshore oil exploration and production projects. The model considers some parameters that aim at capturing the effect of the operators patterns, water depth, duration of seismic research and exploration and drilling work, number of wells, geographic location and geological risk. An estimate is obtained for the additional offshore supply vessels demand, for the period of 2006-2008.

Jr">Floriano C.M. Pires Jr; Augusto R. Antoun

2012-01-01T23:59:59.000Z

50

Behavioral Aspects in Simulating the Future US Building Energy Demand  

E-Print Network [OSTI]

Importance Total off- site energy demand (2030) 20% decreaseImportance Total off-site energy demand (2030) 20% decreaseImportance Total off-site energy demand (2030) 20% decrease

Stadler, Michael

2011-01-01T23:59:59.000Z

51

Improved forecasts of extreme weather events by future space borne Doppler wind lidar  

E-Print Network [OSTI]

sensitive areas. To answer these questions simulation experiments with state-of-the-art numerical weather prediction (NWP) models have proved great value to test future meteorological observing systems a prioriImproved forecasts of extreme weather events by future space borne Doppler wind lidar Gert

Marseille, Gert-Jan

52

Energy dispatch schedule optimization for demand charge reduction using a photovoltaic-battery storage system with solar forecasting  

Science Journals Connector (OSTI)

Abstract A battery storage dispatch strategy that optimizes demand charge reduction in real-time was developed and the discharge of battery storage devices in a grid-connected, combined photovoltaic-battery storage system (PV+ system) was simulated for a summer month, July 2012, and a winter month, November 2012, in an operational environment. The problem is formulated as a linear programming (LP; or linear optimization) routine and daily minimization of peak non-coincident demand is sought to evaluate the robustness, reliability, and consistency of the battery dispatch algorithm. The LP routine leverages solar power and load forecasts to establish a load demand target (i.e., a minimum threshold to which demand can be reduced using a photovoltaic (PV) array and battery array) that is adjusted throughout the day in response to forecast error. The LP routine perfectly minimizes demand charge but forecasts errors necessitate adjustments to the perfect dispatch schedule. The PV+ system consistently reduced non-coincident demand on a metered load that has an elevated diurnal (i.e., daytime) peak. The average reduction in peak demand on weekdays (days that contain the elevated load peak) was 25.6% in July and 20.5% in November. By itself, the PV array (excluding the battery array) reduced the peak demand on average 19.6% in July and 11.4% in November. PV alone cannot perfectly mitigate load spikes due to inherent variability; the inclusion of a storage device reduced the peak demand a further 6.0% in July and 9.3% in November. Circumstances affecting algorithm robustness and peak reduction reliability are discussed.

R. Hanna; J. Kleissl; A. Nottrott; M. Ferry

2014-01-01T23:59:59.000Z

53

Forecasting 65+ travel : an integration of cohort analysis and travel demand modeling  

E-Print Network [OSTI]

Over the next 30 years, the Boomers will double the 65+ population in the United States and comprise a new generation of older Americans. This study forecasts the aging Boomers' travel. Previous efforts to forecast 65+ ...

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

54

Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework  

Science Journals Connector (OSTI)

Abstract Forecasting aggregate demand represents a crucial aspect in all industrial sectors. In this paper, we provide the analytical prediction properties of top-down (TD) and bottom-up (BU) approaches when forecasting the aggregate demand using a multivariate exponential smoothing as demand planning framework. We extend and generalize the results achieved by Widiarta et al. (2009) by employing an unrestricted multivariate framework allowing for interdependency between its variables. Moreover, we establish the necessary and sufficient condition for the equality of mean squared errors (MSEs) of the two approaches. We show that the condition for the equality of \\{MSEs\\} holds even when the moving average parameters of the individual components are not identical. In addition, we show that the relative forecasting accuracy of TD and BU depends on the parametric structure of the underlying framework. Simulation results confirm our theoretical findings. Indeed, the ranking of TD and BU forecasts is led by the parametric structure of the underlying data generation process, regardless of possible misspecification issues.

Giacomo Sbrana; Andrea Silvestrini

2013-01-01T23:59:59.000Z

55

Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting  

Science Journals Connector (OSTI)

Abstract Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 2010–2030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967–2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010–2030.

F.J. Ardakani; M.M. Ardehali

2014-01-01T23:59:59.000Z

56

Quantile Forecasting of Commodity Futures' Returns: Are Implied Volatility Factors Informative?  

E-Print Network [OSTI]

This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied...

Dorta, Miguel

2012-07-16T23:59:59.000Z

57

The outlook for Operations Research: will business education supply enough management science new entrants to meet forecast demand  

Science Journals Connector (OSTI)

Can Management Science in Business Education become sufficiently popular to fill forecast demands for new entrants to its Operations Research (OR) subset? Based upon papers by numerous authors, this paper identifies an interesting phenomenon â?? an increasingly applicable field of Management Science plagued by students avoiding entry. This paper discusses the results of an examination of this phenomenon's background, provides data collected concerning current supply of and projected demand for new entrants in a subset of Management Science; examines the continuing call for new approaches to teaching Management Science as a means of attracting new entrants; and presents continued research suggestions.

Richard A. McMahon; Peter D. DeVries

2012-01-01T23:59:59.000Z

58

Application of neural networking in live cattle futures market: an approach to price-forecasting  

E-Print Network [OSTI]

-Ju Chou, B. S. , Tunghai University, Taiwan Chair of Advisory Committee Dr. John P. Walter The ability to forecast closing price changes using neural networking technique in the live cattle futures market was investigated. Futures prices and contract... volumes from 1977 through 1991 were obtained for four commodities: live cattle, feeder cattle, live hogs and corn. Twelve neural networks were constructed, one for each combination of six contract months and two uading periods. The two trading periods...

Chou, Chien-Ju

2012-06-07T23:59:59.000Z

59

On the forecasting of the challenging world future scenarios  

Science Journals Connector (OSTI)

Logistic and power law methodologies for both retrospective and prospective analyses of extended time series describing evolutionary growth processes, in environments with finite resources, are confronted. While power laws may eventually apply only to the early stages of said growth process, the Allee logistic model seems applicable over the entire span of a long range process. On applying the Allee logistic model to both the world population and the world gross domestic product time series, from 1 to 2008 AD, a projection was obtained that along the next few decades the world should experience a new economic boom phase with the world GDP peaking around the year 2020 and proceeding from then on towards a saturation value of about 142 trillion international dollars, while the world population should reach 8.9 billion people by 2050. These results were then used to forecast the behavior of the supply and consumption of energy and food, two of the main commodities that drive the world system. Our findings suggest that unless the currently prevailing focus on economic growth is changed into that of sustainable prosperity, human society may run into a period of serious economical and social struggles with unpredictable political consequences.

Luiz C.M. Miranda; C.A.S. Lima

2011-01-01T23:59:59.000Z

60

Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices  

E-Print Network [OSTI]

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Demand response computation for future smart grids incorporating wind power  

Science Journals Connector (OSTI)

In this paper, we study supply and demand management in the presence of conventional and renewable energy sources, where the latter is represented by a single wind turbine. Total social welfare, defined in terms of consumer utility and cost of power ... Keywords: constrained optimization, kuhn-tucker conditions, outage probability, renewable source, smart grid

Nihan Çiçek; Hakan Deliç

2013-03-01T23:59:59.000Z

62

Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

DEMAND DEMAND Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future TRANSPORTATION ENERGY FUTURES SERIES: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future A Study Sponsored by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy March 2013 Prepared by CAMBRIDGE SYSTEMATICS Cambridge, MA 02140 under subcontract DGJ-1-11857-01 Technical monitoring performed by NATIONAL RENEWABLE ENERGY LABORATORY Golden, Colorado 80401-3305 managed by Alliance for Sustainable Energy, LLC for the U.S. DEPARTMENT OF ENERGY Under contract DC-A36-08GO28308 This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their

63

Demand Response: Lessons Learned with an Eye to the Future | Department of  

Broader source: Energy.gov (indexed) [DOE]

Demand Response: Lessons Learned with an Eye to the Future Demand Response: Lessons Learned with an Eye to the Future Demand Response: Lessons Learned with an Eye to the Future July 11, 2013 - 11:56am Addthis Patricia A. Hoffman Patricia A. Hoffman Assistant Secretary, Office of Electricity Delivery & Energy Reliability In today's world of limited resources and rising costs, everyone is looking for ways to use what they have more effectively while, at the same time, controlling - and ideally - reducing expenses. The electricity industry is no exception. Through demand response programs such as time-based rates in which customers are offered financial incentives to reduce or shift their consumption during peak periods, utilities are reducing demand and better managing their assets while also giving consumers more options and lowering the cost of electricity. For example,

64

Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast  

E-Print Network [OSTI]

at a relatively slow pace, custom data centers (Google, etc.) are a relatively new end-use that has been seeing................................................................................................................... 7 Alternative Load Forecast Concepts been influenced by expected higher electricity prices that reflect a rapid rise in fuel prices

65

Nuclear Separations for Radiopharmacy:? The Need for Improved Separations To Meet Future Research and Clinical Demands  

Science Journals Connector (OSTI)

Nuclear Separations for Radiopharmacy:? The Need for Improved Separations To Meet Future Research and Clinical Demands ... Chemistry Division, Argonne National Laboratory, Argonne, Illinois 60439, and Department of Chemistry, The University of Alabama, Tuscaloosa, Alabama 35487 ...

Andrew H. Bond; Robin D. Rogers; Mark L. Dietz

2000-07-08T23:59:59.000Z

66

Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis  

Science Journals Connector (OSTI)

The ARIMA(0,1,1) demand model has been analysed extensively by researchers and used widely by forecasting practitioners due to its attractive theoretical properties and empirical evidence in its support. However, no empirical investigations have been conducted in the academic literature to analyse demand forecasting and inventory performance under such a demand model. In this paper, we consider a supply chain formed by a manufacturer and a retailer facing an ARIMA(0,1,1) demand process. The relationship between the forecasting accuracy and inventory performance is analysed along with an investigation on the potential benefits of forecast information sharing between the retailer and the manufacturer. Results are obtained analytically but also empirically by means of experimentation with the sales data related to 329 Stock Keeping Units (SKUs) from a major European superstore. Our analysis contributes towards the development of the current state of knowledge in the areas of inventory forecasting and forecast information sharing and offers insights that should be valuable from the practitioner perspective.

M.Z. Babai; M.M. Ali; J.E. Boylan; A.A. Syntetos

2013-01-01T23:59:59.000Z

67

The relationship between energy intensity and income levels: Forecasting long term energy demand in Asian emerging countries  

SciTech Connect (OSTI)

This paper analyzes long-term trends in energy intensity for ten Asian emerging countries to test for a non-monotonic relationship between energy intensity and income in the author's sample. Energy demand functions are estimated during 1973--1990 using a quadratic function of log income. The long-run coefficient on squared income is found to be negative and significant, indicating a change in trend of energy intensity. The estimates are then used to evaluate a medium-term forecast of energy demand in the Asian countries, using both a log-linear and a quadratic model. It is found that in medium to high income countries the quadratic model performs better than the log-linear, with an average error of 9% against 43% in 1995. For the region as a whole, the quadratic model appears more adequate with a forecast error of 16% against 28% in 1995. These results are consistent with a process of dematerialization, which occurs as a result of a reduction of resource use per unit of GDP once an economy passes some threshold level of GDP per capita.

Galli, R. (Birkbeck Coll., London (United Kingdom) Univ. della Svizzera Italiana, Lugano (Switzerland). Facolta di Scienze Economiche)

1998-01-01T23:59:59.000Z

68

Dynamic forecasting and adaptation for demand optimization in the smart grid  

Science Journals Connector (OSTI)

The daily peaks and valleys in energy demand create inefficiencies and expense in the operation of the electricity grid. Valley periods force utilities to curtail renewable energy sources such as wind as their unpredictable nature makes it difficult ... Keywords: cross-layer, demand optimization, dynamic adaptation, prediction, smart grid

Eamonn O'Toole, Siobhán Clarke

2012-06-01T23:59:59.000Z

69

Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices  

SciTech Connect (OSTI)

On December 9, the reference case projections from ''Annual Energy Outlook 2005 (AEO 2005)'' were posted on the Energy Information Administration's (EIA) web site. As some of you may be aware, we at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past four years, forward natural gas contracts (e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past four years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation (presuming that long-term price stability is valued). In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2005. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or, more recently (and briefly), http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past four AEO releases (AEO 2001-AE0 2004), we once again find that the AEO 2005 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEXAEO 2005 reference case comparison yields by far the largest premium--$1.11/MMBtu levelized over six years--that we have seen over the last five years. In other words, on average, one would have to pay $1.11/MMBtu more than the AEO 2005 reference case natural gas price forecast in order to lock in natural gas prices over the coming six years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation. Fixed-price renewables obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of six years.

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

70

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we once again find that the AEO 2007 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. Specifically, the NYMEX-AEO 2007 premium is $0.73/MMBtu levelized over five years. In other words, on average, one would have had to pay $0.73/MMBtu more than the AEO 2007 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

71

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

72

Renewable Electricity Futures Study. Volume 3: End-Use Electricity Demand  

SciTech Connect (OSTI)

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

Hostick, D.; Belzer, D.B.; Hadley, S.W.; Markel, T.; Marnay, C.; Kintner-Meyer, M.

2012-06-01T23:59:59.000Z

73

Demand forecasting at Zara : a look at seasonality, product lifecycle and cannibalization  

E-Print Network [OSTI]

Zara introduces 10,000 new designs every year and distributes 5.2 million clothing articles per week to a network of over 1925 stores in more than 86 countries. Their high product mix and vast global network makes demand ...

García, José M. (José Manuel)

2014-01-01T23:59:59.000Z

74

Forecasting future oil production in Norway and the UK: a general improved methodology  

E-Print Network [OSTI]

We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 +/- 10 million barrels for Norway and 98 +/- 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

Fievet, Lucas; Cauwels, Peter; Sornette, Didier

2014-01-01T23:59:59.000Z

75

The impact of future energy demand on renewable energy production – Case of Norway  

Science Journals Connector (OSTI)

Abstract Projections of energy demand are an important part of analyses of policies to promote conservation, efficiency, technology implementation and renewable energy production. The development of energy demand is a key driver of the future energy system. This paper presents long-term projections of the Norwegian energy demand as a two-step methodology of first using activities and intensities to calculate a demand of energy services, and secondly use this as input to the energy system model TIMES-Norway to optimize the Norwegian energy system. Long-term energy demand projections are uncertain and the purpose of this paper is to illustrate the impact of different projections on the energy system. The results of the analyses show that decreased energy demand results in a higher renewable fraction compared to an increased demand, and the renewable energy production increases with increased energy demand. The most profitable solution to cover increased demand is to increase the use of bio energy and to implement energy efficiency measures. To increase the wind power production, an increased renewable target or higher electricity export prices have to be fulfilled, in combination with more electricity export.

Eva Rosenberg; Arne Lind; Kari Aamodt Espegren

2013-01-01T23:59:59.000Z

76

Electricity Demand-Side Management for an Energy Efficient Future in China: Technology Options and Policy Priorities  

E-Print Network [OSTI]

Electricity Demand-Side Management for an Energy Efficient Future in China: Technology Options sensitive impacts on electricity demand growth by different demand-side management (DSM) scenarios countries. The research showed that demand side management strategies could result in significant reduction

de Weck, Olivier L.

77

Coal: evolving supply and demand in world seaborne steam coal trade. [1975 to 1985; forecasting to 1995  

SciTech Connect (OSTI)

This paper describes the evolution of world seaborne steam coal trade since 1975. It highlights current trends and the historic and present sources of supply and demand and discusses selected factors that may affect future world trade patterns. It concludes with a general discussion on the prospects for United States participation in the growing world markets for steam coal. Worldwide seaborne steam coal trade is linked very closely to the generation of electricity and industrial use of process heat in cement and other manufacturing plants. The main factors that influence this trade are: economic growth, electricity demand, indigenous coal production (and degree of protection from lower cost coal imports), and the delivered costs of coal relative to other substitutable fuels. It may be of interest to know how these factors have changed seaborne steam coal trade in the past twelve years. In 1970, the total world use of steam coal was about two billion short tons. International trade in steam coal was only 80 million tons or about 4% of the total. Seaborne trade accounted for about 30% of international trade, or about 25 million tons. In 1982, the latest year for which good statistics are available, total world use of steam coal was about 3.6 billion tons. Seaborne steam coal trade was 110 million tons which is about 3% of the total and 37% of the international trade. 11 figs., 2 tabs.

Yancik, J.

1986-01-01T23:59:59.000Z

78

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

This project studied and analyzed Electronic Controls, Inc.’s forecasting process for three high-demand products. In addition, alternative forecasting methods were developed to compare to the current forecast method. The ...

Balandran, Juan

2005-12-16T23:59:59.000Z

79

Project Information Form Project Title White Paper on the Future of Passenger Travel Demand in the United  

E-Print Network [OSTI]

each agency or organization) DOT $26,383.66 Total Project Cost $26,383.66 Agency ID or Contract NumberProject Information Form Project Title White Paper on the Future of Passenger Travel Demand Project This white paper will summarize recent research findings pertaining to future passenger travel

California at Davis, University of

80

An Assessment of Future Demands for and Benefits of Public Transit Services in Tennessee  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

2/55 2/55 An Assessment of Future Demands for and Benefits of Public Transit Services in Tennessee March 2002 Prepared by Frank Southworth David P. Vogt T. Randall Curlee Center for Transportation Analysis Oak Ridge National Laboratory P.O. Box 2008 Oak Ridge, TN 37831 Managed By UT-Battelle, Llc For The U. S. Department Of Energy Under Contract No. DE-AC05-00OR-22725 and Arun Chatterjee Frederick J. Wegmann Civil and Environmental Engineering Department The University of Tennessee Knoxville, TN 37996-2010 Prepared for Office of Public Transportation Tennessee Department of Transportation Nashville, TN 37243 i Contents Page Executive Summary vi 1. Introduction 1.1 1.1 Study Purpose 1.1 1.2 Report Organization and Content 1.2 1.3 Glossary of Terms Used 1.3 2. Transit Benefits Analysis Process 2.1

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Demand response in future power systems management – a conceptual framework and simulation tool.  

E-Print Network [OSTI]

??Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia In competitive electricity markets with deep efficiency concerns, demand response gains significant importance. Moreover, demand response… (more)

Faria, Pedro

2011-01-01T23:59:59.000Z

82

THE FUTURE DEMAND FOR ALTERNATIVE FUEL PASSENGER VEHICLES: A DIFFUSION OF INNOVATION APPROACH  

E-Print Network [OSTI]

.......................................................................................................... 5 2.1 AUTOMOBILE DEMAND MODELS.....................................................................................................................20 2.2.4 The Application of Diffusion Models to Automobile Demand.......................................................................................................................................36 3.1.5 Electric Vehicles

Levinson, David M.

83

Energy Demand Forecast for South East Asia Region: An Econometric Approach with Relation to the Energy Per Capita “Curve”  

Science Journals Connector (OSTI)

Based on the causality analysis completed for the ASEAN region, macroeconomic factors have a strong relation with increasing the power demand. The bi-directional relationship from energy causing the increase of e...

Nuki Agya Utama; Keiichi N. Ishihara; Tetsuo Tezuka…

2013-01-01T23:59:59.000Z

84

Demand side management in smart grid: A review and proposals for future direction  

Science Journals Connector (OSTI)

Abstract This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient.

Linas Gelazanskas; Kelum A.A. Gamage

2014-01-01T23:59:59.000Z

85

Project Information Form Project Title White Paper on the Future of Passenger Travel Demand in the United  

E-Print Network [OSTI]

each agency or organization) Caltrans $26,383 Total Project Cost $26,383 Agency ID or Contract NumberProject Information Form Project Title White Paper on the Future of Passenger Travel Demand DTRT13-G-UTC29 Start and End Dates September 2014 to June 2015 Brief Description of Research Project

California at Davis, University of

86

Demand Responsive Lighting: A Scoping Study  

E-Print Network [OSTI]

3 2.1 Demand-Side Managementbuildings. The demand side management framework is discussedIssues 2.1 Demand-Side Management Framework Forecasting

Rubinstein, Francis; Kiliccote, Sila

2007-01-01T23:59:59.000Z

87

Comparison of Airbus, Boeing, Rolls-Royce and AVITAS market forecasts  

Science Journals Connector (OSTI)

Forecasts of future world demand for commercial aircraft are published fairly regularly by Airbus and Boeing. Other players in the aviation business, Rolls Royce and AVITAS, have also published forecasts in the past year. This article analyses and compares the methods used and assumptions made by the several forecasters. It concludes that there are wide areas of similarity in the approaches used and highlights the most significant area of divergence.

Ralph Anker

2000-01-01T23:59:59.000Z

88

Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market  

Science Journals Connector (OSTI)

This study employs a flexible regime-switching EGARCH model with Student-t distributed error terms to investigate whether volatility regimes and basis affect the behavior of crude oil futures returns, including the conditional mean, variance, skewness, kurtosis as well as the extent of heavy-tailedness. The study also examines whether volatility regimes and asymmetric basis effects can improve the forecasting accuracy. The main merit of the empirical model is that the basis effect is allowed to be asymmetric and to vary across volatility regimes. Empirical results suggest that the conditional mean and variance respond to the basis asymmetrically and nonlinearly, and that the responses of transition probabilities to the basis are symmetric. Furthermore, the conditional higher moments are sensitive to the absolute value of basis, and the heavy tailed characteristic can be greatly alleviated by taking into account the asymmetric basis effects and regime switches. Finally, the regime switches and asymmetric basis effects play decisive roles in forecasting return, volatility and tail distribution.

Kuang-Liang Chang

2012-01-01T23:59:59.000Z

89

4 - Future industrial coal utilization: forecasts and emerging technological and regulatory issues  

Science Journals Connector (OSTI)

Abstract: Coal production and utilization will grow substantially in the future. This chapter starts by describing coal production and consumption, with a focus on future trends. A discussion of major technology and regulatory issues for coal-fired power plants and the production of metallurgical coal then follows.

J.K. Alderman

2013-01-01T23:59:59.000Z

90

A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey  

Science Journals Connector (OSTI)

Abstract The existing electricity systems have been substantially designed to allow only centralized power generation and unidirectional power flow. Therefore, the objective of improving the conventional power systems with the capabilities of decentralized generation and advanced control has conflicted with the present power system infrastructure and thus a profound change has necessitated in the current power grids. To that end, the concept of smart grid has been introduced at the last decades in order to accomplish the modernization of the power grid while incorporating various capabilities such as advanced metering, monitoring and self-healing to the systems. Among the various advanced components in smart grid structure, “smart home” is of vital importance due to its handling difficulties caused by the stochastic behaviors of inhabitants. However, limited studies concerning the implementation of smart homes have so far been reported in the literature. Motivated by this need, this paper investigates an experimental smart home with various renewable energy sources and storage systems in terms of several aspects such as in-home energy management, appliances control and power flow. Furthermore, the study represents one of the very first attempts to evaluate the contribution of power forecasting of renewable energy sources on the performance of smart home concepts.

A. Tascikaraoglu; A.R. Boynuegri; M. Uzunoglu

2014-01-01T23:59:59.000Z

91

Forecasting the Standard & Poor's 500 stock index futures price: interest rates, dividend yields, and cointegration  

E-Print Network [OSTI]

Daily Standard & Poor's 500 stock index cash and futures prices are studies in a cointegration framework using Johansen's maximum likelihood procedure. To account for the time varying relationship(basis) between the two markets, a theoretical...

Fritsch, Roger Erwin

1997-01-01T23:59:59.000Z

92

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

Forecast Evaluation 2005 Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 * Then Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose or advocate future legislative and regulatory changes. All laws are assumed to remain as currently enacted; however, the impacts of emerging regulatory changes, when defined, are reflected.

93

Analysis of the impacts of building energy efficiency policies and technical improvements on China's future energy demand  

Science Journals Connector (OSTI)

In this paper, the LEAP (Long-range Energy Alternatives Planning system) 2000 model and scenario analysis were utilised to study the impact of implementing building energy efficiency policies and promoting related technical improvements on China's future building energy demand up to 2020. In the coming 20 years, China's building energy consumption is expected to increase and will be the main contributor to the growth in China's future energy demand. Without the rational induction of energy efficiency and environmental policies, China's building energy consumption may reach 860 Mtce in 2020 from 197 Mtce in 2000. On the other hand, China possesses huge energy saving potential in the building area. With the enforcement and adoption of related building energy efficiency policies and technical improvement measures, energy consumption in the building sector might decrease to 480 Mtce by 2020; and the energy saving potential might reach 380 Mtce.

Kang Yanbing; Wei Qingpeng

2005-01-01T23:59:59.000Z

94

Assessing the impacts of future demand for saline groundwater on commercial deployment of CCS in the United States  

SciTech Connect (OSTI)

This paper provides a preliminary assessment of the potential impact that future demand for groundwater might have on the commercial deployment of carbon dioxide capture and storage (CCS) technologies within the United States. A number of regions within the U.S. have populations, agriculture and industries that are particularly dependent upon groundwater. Moreover, some key freshwater aquifers are already over-utilized or depleted, and others are likely to be moving toward depletion as demand grows. The need to meet future water demands may lead some parts of the nation to consider supplementing existing supplies with lower quality groundwater resources, including brackish waters that are currently not considered sources of drinking water but which could provide supplemental water via desalination. In some areas, these same deep saline-filled geologic formations also represent possible candidate carbon dioxide (CO2) storage reservoirs. The analysis presented here suggests that future constraints on CCS deployment due to potential needs to supplement conventional water supplies by desalinating deeper and more brackish waters are likely to be necessary only in limited regions across the country, particularly in areas that are already experiencing water stress.

Davidson, Casie L.; Dooley, James J.; Dahowski, Robert T.

2009-04-20T23:59:59.000Z

95

Energy demand  

Science Journals Connector (OSTI)

The basic forces pushing up energy demand are population increase and economic growth. From ... of these it is possible to estimate future energy requirements.

Geoffrey Greenhalgh

1980-01-01T23:59:59.000Z

96

1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.  

SciTech Connect (OSTI)

This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

97

International Oil Supplies and Demands  

SciTech Connect (OSTI)

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

Not Available

1991-09-01T23:59:59.000Z

98

International Oil Supplies and Demands  

SciTech Connect (OSTI)

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

Not Available

1992-04-01T23:59:59.000Z

99

Impact of past and future residential housing development patterns on energy demand and related emissions  

Science Journals Connector (OSTI)

Strategies to meet the needs of Melbourne’s future residents, while restricting greenhouse gas emissions, have been proposed. These include increasing public transport patronage to 20% and mandating the energy ef...

R. J. Fuller; R. H. Crawford

2011-06-01T23:59:59.000Z

100

Wind Power Generation’s Impact on Peak Time Demand and on Future Power Mix  

Science Journals Connector (OSTI)

Although wind power is regarded as one of the ways to actively respond to climate change, the stability of the whole power system could be a serious problem in the future due to wind power’s uncertainties. These ...

Jinho Lee; Suduk Kim

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

102

Demand response enabling technology development  

E-Print Network [OSTI]

behavior in developing a demand response future. Phase_II_Demand Response Enabling Technology Development Phase IIYi Yuan The goal of the Demand Response Enabling Technology

Arens, Edward; Auslander, David; Huizenga, Charlie

2008-01-01T23:59:59.000Z

103

California Energy Demand Scenario Projections to 2050  

E-Print Network [OSTI]

California Energy Demand Scenario Projections to 2050 RyanCEC (2003a) California energy demand 2003-2013 forecast.CEC (2005a) California energy demand 2006-2016: Staff energy

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

104

Electricity price forecasting: A review of the state-of-the-art with a look into the future  

Science Journals Connector (OSTI)

Abstract A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.

Rafa? Weron

2014-01-01T23:59:59.000Z

105

RACORO Forecasting  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Daniel Hartsock CIMMS, University of Oklahoma ARM AAF Wiki page Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance...

106

International Oil Supplies and Demands. Volume 1  

SciTech Connect (OSTI)

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

Not Available

1991-09-01T23:59:59.000Z

107

International Oil Supplies and Demands. Volume 2  

SciTech Connect (OSTI)

The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

Not Available

1992-04-01T23:59:59.000Z

108

Aviation fuel demand development in China  

Science Journals Connector (OSTI)

Abstract This paper analyzes the core factors and the impact path of aviation fuel demand in China and conducts a structural decomposition analysis of the aviation fuel cost changes and increase of the main aviation enterprises’ business profits. Through the establishment of an integrated forecast model for China’s aviation fuel demand, this paper confirms that the significant rise in China’s aviation fuel demand because of increasing air services demand is more than offset by higher aviation fuel efficiency. There are few studies which use a predictive method to decompose, estimate and analyze future aviation fuel demand. Based on a structural decomposition with indirect prediction, aviation fuel demand is decomposed into efficiency and total amount (aviation fuel efficiency and air transport total turnover). The core influencing factors for these two indexes are selected using path analysis. Then, univariate and multivariate models (ETS/ARIMA model and Bayesian multivariate regression) are used to analyze and predict both aviation fuel efficiency and air transport total turnover. At last, by integrating results, future aviation fuel demand is forecast. The results show that the aviation fuel efficiency goes up by 0.8% as the passenger load factor increases 1%; the air transport total turnover goes up by 3.8% and 0.4% as the urbanization rate and the per capita GDP increase 1%, respectively. By the end of 2015, China’s aviation fuel demand will have increased to 28 million tonnes, and is expected to be 50 million tonnes by 2020. With this in mind, increases in the main aviation enterprises’ business profits must be achieved through the further promotion of air transport.

Jian Chai; Zhong-Yu Zhang; Shou-Yang Wang; Kin Keung Lai; John Liu

2014-01-01T23:59:59.000Z

109

The future of oil: Geology versus technology  

Science Journals Connector (OSTI)

Abstract We discuss and reconcile the geological and economic/technological views concerning the future of world oil production and prices, and present a nonlinear econometric model of the world oil market that encompasses both views. The model performs far better than existing empirical models in forecasting oil prices and oil output out-of-sample. Its point forecast is for a near doubling of the real price of oil over the coming decade, though the error bands are wide, reflecting sharply differing judgments on the ultimately recoverable reserves, and on future price elasticities of oil demand and supply.

Jaromir Benes; Marcelle Chauvet; Ondra Kamenik; Michael Kumhof; Douglas Laxton; Susanna Mursula; Jack Selody

2015-01-01T23:59:59.000Z

110

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

...of each variable on GDP (13, 17), combined with global GDP projections (14...population, and per capita GDP, combined with projected...measure of agricultural demand for water, is forecast...Just as demand for energy is the major cause...

David Tilman; Joseph Fargione; Brian Wolff; Carla D'Antonio; Andrew Dobson; Robert Howarth; David Schindler; William H. Schlesinger; Daniel Simberloff; Deborah Swackhamer

2001-04-13T23:59:59.000Z

111

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

112

How Can China Lighten Up? Urbanization, Industrialization and Energy Demand Scenarios  

E-Print Network [OSTI]

on the forecast of total energy demand. Based on this, weadjustment spurred energy demand for construction of newenergy services. Primary energy demand grew at an average

Aden, Nathaniel T.

2010-01-01T23:59:59.000Z

113

Electricity demand and supply projections for Indian economy  

Science Journals Connector (OSTI)

The present paper deals with an econometric model to forecast future electricity requirements for various sectors of Indian economy. Following the analysis of time series of sectoral GDPs, number of consumers in various sectors and price indices of electricity, a logarithmic linear regression model has been developed to forecast long-term demand of electricity up to the year 2045. Using the historical GDP growth in various sectors and the corresponding electricity consumption for the period 1971-2005, it is predicted that the total electricity demand will be 5000 billion kWh, against a supply of 1500 billion kWh in the year 2045. This may lead to a disastrous situation for the country unless drastic policy measures are taken to improve the supply side as well as to reduce demand.

Subhash Mallah; N.K. Bansal

2009-01-01T23:59:59.000Z

114

Electricity demand-side management for an energy efficient future in China : technology options and policy priorities  

E-Print Network [OSTI]

The main objective of this research is to identify robust technology and policy options which achieve substantial reductions in electricity demand in China's Shandong Province. This research utilizes a scenario-based ...

Cheng, Chia-Chin

2005-01-01T23:59:59.000Z

115

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

This paper addresses hierarchical forecasting in a production planning environment. Specifically, we examine the relative effectiveness of Top-Down (TD) and Bottom-Up (BU) strategies for forecasting the demand for a substitutable product (which belongs to a family) as well as the demand for the product family under different types of family demand processes. Through a simulation study, it is revealed that the TD strategy consistently outperforms the BU strategy for forecasting product family demand. The relative superiority of the TD strategy further improves by as much as 52% as the product demand variability increases and the degree of substitutability between the products decreases. This phenomenon, however, is not always true for forecasting the demand for the products within the family. In this case, it is found that there are a few situations wherein the BU strategy marginally outperforms the TD strategy, especially when the product demand variability is high and the degree of product substitutability is low.

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

116

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

4 4 The commercial module forecasts consumption by fuel 15 at the Census division level using prices from the NEMS energy supply modules, and macroeconomic variables from the NEMS Macroeconomic Activity Module (MAM), as well as external data sources (technology characterizations, for example). Energy demands are forecast for ten end-use services 16 for eleven building categories 17 in each of the nine Census divisions (see Figure 5). The model begins by developing forecasts of floorspace for the 99 building category and Census division combinations. Next, the ten end-use service demands required for the projected floorspace are developed. The electricity generation and water and space heating supplied by distributed generation and combined heat and power technologies are projected. Technologies are then

117

A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting  

Science Journals Connector (OSTI)

In existing researches, the investigations of oil price volatility are always performed based on daily data and squared daily return is always taken as the proxy of actual volatility. However, it is widely accepted that the popular realized volatility (RV) based on high frequency data is a more robust measure of actual volatility than squared return. Due to this motivation, we investigate dynamics of daily volatility of Shanghai fuel oil futures prices employing 5-minute high frequency data. First, using a nonparametric method, we find that RV displays strong long-range dependence and recent financial crisis can cause a lower degree of long-range dependence. Second, we model daily volatility using RV models and GARCH-class models. Our results indicate that RV models for intraday data overwhelmingly outperform GARCH-class models for daily data in forecasting fuel oil price volatility, regardless the proxy of actual volatility. Finally, we investigate the major source of such volatile prices and found that trader activity has major contribution to fierce variations of fuel oil prices.

Li Liu; Jieqiu Wan

2012-01-01T23:59:59.000Z

118

California Baseline Energy Demands to 2050 for Advanced Energy Pathways  

E-Print Network [OSTI]

ED2, September. CEC (2005b) Energy demand forecast methodsCalifornia Baseline Energy Demands to 2050 for Advancedof a baseline scenario for energy demand in California for a

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

119

Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia  

Science Journals Connector (OSTI)

Abstract In the light of European energy-climate package and its measures for increasing security of supply, decreasing the impact on environment and stimulating sustainability, Croatia as a new EU (European Union) member state needs to reconsider and develop new energy policy towards energy efficiency and renewable energy sources. Croatian long-term energy demand and its effect on the future national GHG (greenhouse gas) emissions are analysed in this paper. For that purpose the NeD model was constructed (National energy demand model). The model is comprised out of six modules, each representing one sector: industry, transport, households, services, agriculture and construction. The model is based on bottom up approach. The analysis has shown that energy policy measures, identified through this paper, can potentially achieve energy savings up to 157 PJ in the year 2050, which presents a 40% decrease to referent (frozen efficiency) scenario. Results obtained in this paper were also compared to the Croatian National Energy Strategy for the years 2020 and 2030. It was shown that if already implemented policies were properly taken into account the actual final energy demand for the year 2030 would be 43% lower than projected by the Croatian National Energy Strategy.

Tomislav Pukšec; Brian Vad Mathiesen; Tomislav Novosel; Neven Dui?

2014-01-01T23:59:59.000Z

120

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS 27 data.

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

The National Energy Modeling System: An Overview 2000 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar and geothermal energy. RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts, which are used by RDM to develop forecasts of energy consumption by fuel and Census division. residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar and geothermal energy. RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts, which are used by RDM to develop forecasts of energy consumption by fuel and Census division. Figure 5. Residential Demand Module Structure RDM incorporates the effects of four broadly-defined determinants of energy consumption: economic and demographic effects, structural effects, technology turnover and advancement effects, and energy market effects. Economic and demographic effects include the number, dwelling type (single-family, multi-family or mobile homes), occupants per household, and location of housing units. Structural effects include increasing average dwelling size and changes in the mix of desired end-use services provided by energy (new end uses and/or increasing penetration of current end uses, such as the increasing popularity of electronic equipment and computers). Technology effects include changes in the stock of installed equipment caused by normal turnover of old, worn out equipment with newer versions which tend to be more energy efficient, the integrated effects of equipment and building shell (insulation level) in new construction, and in the projected availability of even more energy-efficient equipment in the future. Energy market effects include the short-run effects of energy prices on energy demands, the longer-run effects of energy prices on the efficiency of purchased equipment and the efficiency of building shells, and limitations on minimum levels of efficiency imposed by legislated efficiency standards.

122

Assumptions to the Annual Energy Outlook 2002 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and

123

Assumptions to the Annual Energy Outlook 2001 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and

124

Strategic implications for US - Persian Gulf relations on domestic and worldwide oil production for future US oil demand. Final report  

SciTech Connect (OSTI)

The U.S. dependence on oil imports is examined in light of current U.S. oil production, its potential for future discoveries, and the availability of oil products form Venezuela, Mexico, and other South American countries. There is no geologic reason why the U.S. cannot continue to replace its reserves consumed annually, continue conservation efforts reducing its import dependence, and shift its foreign oil supply closer to home, i.e., Mexico and South America. Increasing the price of oil domestically ensures continued exploration, and shifting the source of imports reduces the length of SLOC'S carrying critical oil products.

Kaplan, S.S.

1987-03-01T23:59:59.000Z

125

World Energy Use — Trends in Demand  

Science Journals Connector (OSTI)

In order to provide adequate energy supplies in the future, trends in energy demand must be evaluated and projections of future demand developed. World energy use is far from static, and an understanding of the demand

Randy Hudson

1996-01-01T23:59:59.000Z

126

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

: Technology Forecast, Laws of Technical systems evolution, Analysis of Contradictions. 1. Introduction Let us: If technology forecasting practice remains at the present level, it is necessary to significantly improve to new demands (like Green House Gases - GHG Effect reduction or covering exploded nuclear reactor

Paris-Sud XI, Université de

127

Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West; Executive Summary  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

National Renewable Energy Laboratory 15013 Denver West Parkway Golden, CO 80401 303-275-3000 * www.nrel.gov Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West Executive Summary David J. Hurlbut, Joyce McLaren, and Rachel Gelman National Renewable Energy Laboratory Prepared under Task No. AROE.2000 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Technical Report NREL/TP-6A20-57830 August 2013 Contract No. DE-AC36-08GO28308

128

Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

(This page intentionally left blank) (This page intentionally left blank) National Renewable Energy Laboratory 15013 Denver West Parkway Golden, CO 80401 303-275-3000 * www.nrel.gov Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West David J. Hurlbut, Joyce McLaren, and Rachel Gelman National Renewable Energy Laboratory Prepared under Task No. AROE.2000 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Technical Report NREL/TP-6A20-57830 August 2013 Contract No. DE-AC36-08GO28308

129

Assumptions to the Annual Energy Outlook 1999 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

residential.gif (5487 bytes) residential.gif (5487 bytes) The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions. The Residential Demand Module also requires projections of available equipment over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

130

Assumptions to the Annual Energy Outlook 2000 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions. The Residential Demand Module also requires projections of available equipment over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

131

Nuclear energy acceptance and potential role to meet future energy demand. Which technical/scientific achievements are needed?  

Science Journals Connector (OSTI)

25 years after Chernobyl the Fukushima disaster has changed the perspectives of nuclear power. The disaster has shed a negative light on the independence reliability and rigor of the national nuclear regulator and plant operator and the usefulness of the international IAEA guidelines on nuclear safety. It has become clear that in the light of the most severe earthquake in the history of Japan the plants at Fukushima Daiichi were not adequately protected against tsunamis. Nuclear acceptance has suffered enormously and has changed the perspectives of nuclear energy dramatically in countries that have a very risk-sensitive population Germany is an example. The paper analyses the reactions in major countries and the expected impact on future deployment of reactors and on R&D activities. On the positive side the disaster has demonstrated a remarkable robustness of most of the 14 reactors closest to the epicentre of the Tohoku Seaquake although not designed to an event of level 9.0. Public acceptance can only be regained with a rigorous and worldwide approach towards inherent reactor safety and design objectives that limit the impact of severe accidents to the plant itself (like many of the new Gen III reactors). A widespread release of radioactivity and the evacuation (temporary or permanent) of the population up to 30 km around a facility are simply not acceptable. Several countries have announced to request more stringent international standards for reactor safety. The IAEA should take this move forward and intensify and strengthen the different peer review mission schemes. The safety guidelines and peer reviews should in fact become legally binding for IAEA members. The paper gives examples of the new safety features developed over the last 20 years and which yield much safer reactors with lesser burden to the environment under severe accident conditions. The compatibility of these safety systems with the current concepts for fusion-fission hybrids which have recently been proposed for energy production is critically reviewed. There are major challenges remaining that are shortly outlined. Scientific/technical achievements that are required in the light of the Fukushima accident are highlighted.

2012-01-01T23:59:59.000Z

132

Nuclear energy acceptance and potential role to meet future energy demand. Which technical/scientific achievements are needed?  

SciTech Connect (OSTI)

25 years after Chernobyl, the Fukushima disaster has changed the perspectives of nuclear power. The disaster has shed a negative light on the independence, reliability and rigor of the national nuclear regulator and plant operator and the usefulness of the international IAEA guidelines on nuclear safety. It has become clear that, in the light of the most severe earthquake in the history of Japan, the plants at Fukushima Daiichi were not adequately protected against tsunamis. Nuclear acceptance has suffered enormously and has changed the perspectives of nuclear energy dramatically in countries that have a very risk-sensitive population, Germany is an example. The paper analyses the reactions in major countries and the expected impact on future deployment of reactors and on R and D activities. On the positive side, the disaster has demonstrated a remarkable robustness of most of the 14 reactors closest to the epicentre of the Tohoku Seaquake although not designed to an event of level 9.0. Public acceptance can only be regained with a rigorous and worldwide approach towards inherent reactor safety and design objectives that limit the impact of severe accidents to the plant itself (like many of the new Gen III reactors). A widespread release of radioactivity and the evacuation (temporary or permanent) of the population up to 30 km around a facility are simply not acceptable. Several countries have announced to request more stringent international standards for reactor safety. The IAEA should take this move forward and intensify and strengthen the different peer review mission schemes. The safety guidelines and peer reviews should in fact become legally binding for IAEA members. The paper gives examples of the new safety features developed over the last 20 years and which yield much safer reactors with lesser burden to the environment under severe accident conditions. The compatibility of these safety systems with the current concepts for fusion-fission hybrids, which have recently been proposed for energy production, is critically reviewed. There are major challenges remaining that are shortly outlined. Scientific/technical achievements that are required in the light of the Fukushima accident are highlighted.

Schenkel, Roland [European Commission, Joint Research Centre, Institute for Transuranium Elements, Hermann-von-Helmholtz-Platz 1,76344 Eggenstein-Leopoldshafen (Germany)

2012-06-19T23:59:59.000Z

133

Demand forecasting for aircraft engine aftermarket  

E-Print Network [OSTI]

In 2006, Pratt and Whitney launched the Global Material Solutions business model aiming to supply spare parts to non-OEM engines with minimum 95% on-time delivery and fill-rate. Lacking essential technical knowledge of the ...

Ho, Kien K. (Kine Kit)

2008-01-01T23:59:59.000Z

134

Development and application of econometric demand and supply models for selected Chesapeake Bay seafood products  

SciTech Connect (OSTI)

Five models were developed to forecast future Chesapeake seafood product prices, harvest quantities, and resulting income. Annual econometric models are documented for oysters, hard and soft blue crabs, and hard and soft clams. To the degree that data permit, these models represent demand and supply at the retail, wholesale, and harvest levels. The resulting models have broad applications in environmental policy issues and regulatory analyses for the Chesapeake Bay. 37 references, 10 figures, 99 tables.

Nieves, L.A.; Moe, R.J.

1984-12-01T23:59:59.000Z

135

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

Different spare parts are associated with different underlying demand patterns, which in turn require different forecasting methods. Consequently, there is a need to categorise stock keeping units (SKUs) and apply the most appropriate methods in each category. For intermittent demands, Croston's method (CRO) is currently regarded as the standard method used in industry to forecast the relevant inventory requirements; this is despite the bias associated with Croston's estimates. A bias adjusted modification to CRO (Syntetos–Boylan Approximation, SBA) has been shown in a number of empirical studies to perform very well and be associated with a very ‘robust’ behaviour. In a 2005 article, entitled ‘On the categorisation of demand patterns’ published by the Journal of the Operational Research Society, Syntetos et al. (2005) suggested a categorisation scheme, which establishes regions of superior forecasting performance between CRO and SBA. The results led to the development of an approximate rule that is expressed in terms of fixed cut-off values for the following two classification criteria: the squared coefficient of variation of the demand sizes and the average inter-demand interval. Kostenko and Hyndman (2006) revisited this issue and suggested an alternative scheme to distinguish between CRO and SBA in order to improve overall forecasting accuracy. Claims were made in terms of the superiority of the proposed approach to the original solution but this issue has never been assessed empirically. This constitutes the main objective of our work. In this paper the above discussed classification solutions are compared by means of experimentation on more than 10,000 \\{SKUs\\} from three different industries. The results enable insights to be gained into the comparative benefits of these approaches. The trade-offs between forecast accuracy and other implementation related considerations are also addressed.

G. Heinecke; A.A. Syntetos; W. Wang

2013-01-01T23:59:59.000Z

136

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

of a range of world oil prices for future energy demand andTo examine the ef feet of oil prices on energy demand andprojections of world oil prices. Th and demand. determined

Sathaye, Jayant

2013-01-01T23:59:59.000Z

137

Drivers of Future Energy Demand  

Gasoline and Diesel Fuel Update (EIA)

trends - Household income migration urbanization * Policy: China Energy Outlook - Air pollution - Climate change 4 (1) Industrial energy intensity: The energy intensity of...

138

Transportation energy demand: Model development and use  

Science Journals Connector (OSTI)

This paper describes work undertaken and sponsored by the Energy Commission to improve transportation energy demand forecasting and policy analysis for California. Two ... , the paper discusses some of the import...

Chris Kavalec

1998-06-01T23:59:59.000Z

139

EIA-Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2007 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new

140

Demand Reduction  

Broader source: Energy.gov [DOE]

Grantees may use funds to coordinate with electricity supply companies and utilities to reduce energy demands on their power systems. These demand reduction programs are usually coordinated through...

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Oxygenate Supply/Demand Balances  

Gasoline and Diesel Fuel Update (EIA)

Oxygenate Supply/Demand Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model By Tancred C.M. Lidderdale This article first appeared in the Short-Term Energy Outlook Annual Supplement 1995, Energy Information Administration, DOE/EIA-0202(95) (Washington, DC, July 1995), pp. 33-42, 83-85. The regression results and historical data for production, inventories, and imports have been updated in this presentation. Contents * Introduction o Table 1. Oxygenate production capacity and demand * Oxygenate demand o Table 2. Estimated RFG demand share - mandated RFG areas, January 1998 * Fuel ethanol supply and demand balance o Table 3. Fuel ethanol annual statistics * MTBE supply and demand balance o Table 4. EIA MTBE annual statistics * Refinery balances

142

Forecast Prices  

Gasoline and Diesel Fuel Update (EIA)

Notes: Notes: Prices have already recovered from the spike, but are expected to remain elevated over year-ago levels because of the higher crude oil prices. There is a lot of uncertainty in the market as to where crude oil prices will be next winter, but our current forecast has them declining about $2.50 per barrel (6 cents per gallon) from today's levels by next October. U.S. average residential heating oil prices peaked at almost $1.50 as a result of the problems in the Northeast this past winter. The current forecast has them peaking at $1.08 next winter, but we will be revisiting the outlook in more detail next fall and presenting our findings at the annual Winter Fuels Conference. Similarly, diesel prices are also expected to fall. The current outlook projects retail diesel prices dropping about 14 cents per gallon

143

Investigation of rolling horizon flexibility contracts in a supply chain under highly variable stochastic demand  

Science Journals Connector (OSTI)

......research-article Articles Demand Forecasting for Inventory Management Investigation of rolling...variable stochastic demand Patrick M. Walsh Peter...and supplier (CM) side of the RHF contract...the stochastic market demand. 3. Model description......

Patrick M. Walsh; Peter A. Williams; Cathal Heavey

2008-04-01T23:59:59.000Z

144

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: * Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the

145

Improving the forecasting function for a Credit Hire operator in the UK  

Science Journals Connector (OSTI)

This study aims to test on the predictability of Credit Hire services for the automobile and insurance industry. A relatively sophisticated time series forecasting procedure, which conducts a competition among exponential smoothing models, is employed to forecast demand for a leading UK Credit Hire operator (CHO). The generated forecasts are compared against the Naive method, resulting that demand for CHO services is indeed extremely hard to forecast, as the underlying variable is the number of road accidents – a truly stochastic variable.

Nicolas D. Savio; K. Nikolopoulos; Konstantinos Bozos

2009-01-01T23:59:59.000Z

146

Designing future cities | Argonne National Laboratory  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

power streaming into our homes and businesses is a balanced coordination between energy suppliers and energy producers. To provide planners with better energy demand forecasts...

147

National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment  

SciTech Connect (OSTI)

The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

1982-03-31T23:59:59.000Z

148

Forecasting the Market Penetration of Energy Conservation Technologies: The Decision Criteria for Choosing a Forecasting Model  

E-Print Network [OSTI]

An important determinant of our energy future is the rate at which energy conservation technologies, once developed, are put into use. At Synergic Resources Corporation, we have adapted and applied a methodology to forecast the use of conservation...

Lang, K.

1982-01-01T23:59:59.000Z

149

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

In this paper is presented a web tool for electricity forecast for Kosovo market for the upcoming ten years. The input data i.e. electricity generation capacities, demand and consume are taken from the document "Kosovo Energy Strategy 2009-2018" compiled ... Keywords: .NET, database, electricity forecast, internet, simulation, web

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

150

Mercury emissions from municipal solid waste combustors. An assessment of the current situation in the United States and forecast of future emissions  

SciTech Connect (OSTI)

This report examines emissions of mercury (Hg) from municipal solid waste (MSW) combustion in the United States (US). It is projected that total annual nationwide MSW combustor emissions of mercury could decrease from about 97 tonnes (1989 baseline uncontrolled emissions) to less than about 4 tonnes in the year 2000. This represents approximately a 95 percent reduction in the amount of mercury emitted from combusted MSW compared to the 1989 mercury emissions baseline. The likelihood that routinely achievable mercury emissions removal efficiencies of about 80 percent or more can be assured; it is estimated that MSW combustors in the US could prove to be a comparatively minor source of mercury emissions after about 1995. This forecast assumes that diligent measures to control mercury emissions, such as via use of supplemental control technologies (e.g., carbon adsorption), are generally employed at that time. However, no present consensus was found that such emissions control measures can be implemented industry-wide in the US within this time frame. Although the availability of technology is apparently not a limiting factor, practical implementation of necessary control technology may be limited by administrative constraints and other considerations (e.g., planning, budgeting, regulatory compliance requirements, etc.). These projections assume that: (a) about 80 percent mercury emissions reduction control efficiency is achieved with air pollution control equipment likely to be employed by that time; (b) most cylinder-shaped mercury-zinc (CSMZ) batteries used in hospital applications can be prevented from being disposed into the MSW stream or are replaced with alternative batteries that do not contain mercury; and (c) either the amount of mercury used in fluorescent lamps is decreased to an industry-wide average of about 27 milligrams of mercury per lamp or extensive diversion from the MSW stream of fluorescent lamps that contain mercury is accomplished.

none,

1993-05-01T23:59:59.000Z

151

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation Evaluation Annual Energy Outlook Forecast Evaluation by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the impacts were not incorporated in the AEO projections until their enactment or effective dates in accordance with EIA's requirement to remain policy neutral and include only current laws and regulations in the AEO reference case projections.

152

Combination of Long Term and Short Term Forecasts, with Application to Tourism  

E-Print Network [OSTI]

Combination of Long Term and Short Term Forecasts, with Application to Tourism Demand Forecasting that are combined. As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we con- sider 33 source countries, as well as the aggregate. The novel

Abu-Mostafa, Yaser S.

153

The National Energy Modeling System: An Overview 2000 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

industrial demand module (IDM) forecasts energy consumption for fuels and feedstocks for nine manufacturing industries and six nonmanufactur- ing industries, subject to delivered prices of energy and macroeconomic variables representing the value of output for each industry. The module includes industrial cogeneration of electricity that is either used in the industrial sector or sold to the electricity grid. The IDM structure is shown in Figure 7. industrial demand module (IDM) forecasts energy consumption for fuels and feedstocks for nine manufacturing industries and six nonmanufactur- ing industries, subject to delivered prices of energy and macroeconomic variables representing the value of output for each industry. The module includes industrial cogeneration of electricity that is either used in the industrial sector or sold to the electricity grid. The IDM structure is shown in Figure 7. Figure 7. Industrial Demand Module Structure Industrial energy demand is projected as a combination of “bottom up” characterizations of the energy-using technology and “top down” econometric estimates of behavior. The influence of energy prices on industrial energy consumption is modeled in terms of the efficiency of use of existing capital, the efficiency of new capital acquisitions, and the mix of fuels utilized, given existing capital stocks. Energy conservation from technological change is represented over time by trend-based “technology possibility curves.” These curves represent the aggregate efficiency of all new technologies that are likely to penetrate the future markets as well as the aggregate improvement in efficiency of 1994 technology.

154

A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power  

Science Journals Connector (OSTI)

Abstract As a form of renewable and low-carbon energy resource, wind power is anticipated to play an essential role in the future energy structure. Whereas, its features of time mismatch with power demand and uncertainty pose barriers for the power system to utilize it effectively. Hence, a novel unit commitment model is proposed in this paper considering demand response and electric vehicles, which can promote the exploitation of wind power. On the one hand, demand response and electric vehicles have the feasibility to change the load demand curve to solve the mismatch problem. On the other hand, they can serve as reserve for wind power. To deal with the unit commitment problem, authors use a fuzzy chance-constrained program that takes into account the wind power forecasting errors. The numerical study shows that the model can promote the utilization of wind power evidently, making the power system operation more eco-friendly and economical.

Ning Zhang; Zhaoguang Hu; Xue Han; Jian Zhang; Yuhui Zhou

2015-01-01T23:59:59.000Z

155

Demand models for U.S. domestic air passenger markets  

E-Print Network [OSTI]

The airline industry in recent years has suffered from the adverse effects of top level planning decisions based upon inaccurate demand forecasts. The air carriers have recognized the immediate need to develop their ...

Eriksen, Steven Edward

1978-01-01T23:59:59.000Z

156

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System  

SciTech Connect (OSTI)

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This report serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, section 57(b)(1)). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1995-02-01T23:59:59.000Z

157

Model documentation report: Commercial sector demand module of the national energy modeling system  

SciTech Connect (OSTI)

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This document serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1994-08-01T23:59:59.000Z

158

Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West; Report and Executive Summary  

SciTech Connect (OSTI)

This study assesses the outlook for utility-scale renewable energy development in the West once states have met their renewable portfolio standard (RPS) requirements. In the West, the last state RPS culminates in 2025, so the analysis uses 2025 as a transition point on the timeline of RE development. Most western states appear to be on track to meet their final requirements, relying primarily on renewable resources located relatively close to the customers being served. What happens next depends on several factors including trends in the supply and price of natural gas, greenhouse gas and other environmental regulations, consumer preferences, technological breakthroughs, and future public policies and regulations. Changes in any one of these factors could make future renewable energy options more or less attractive.

Hurlbut, D. J.; McLaren, J.; Gelman, R.

2013-08-01T23:59:59.000Z

159

Future Impacts of Coal Distribution Constraints on Coal Cost  

E-Print Network [OSTI]

transportation component of coal price should also increase;investment. Coal costs and prices are functions of a numberto forecast coal demand, supply, and prices from now to

McCollum, David L

2007-01-01T23:59:59.000Z

160

Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,  

E-Print Network [OSTI]

Leveraging Weather Forecasts in Renewable Energy Systems Navin Sharmaa, , Jeremy Gummesonb , David, Binghamton, NY 13902 Abstract Systems that harvest environmental energy must carefully regulate their us- age to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since

Shenoy, Prashant

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems  

E-Print Network [OSTI]

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin Sharma,gummeson,irwin,shenoy}@cs.umass.edu Abstract--To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands

Shenoy, Prashant

162

Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type

163

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer University, D-26111 Oldenburg Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. Fluctuations of solar irradiance

Heinemann, Detlev

164

Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

page intentionally left blank page intentionally left blank 69 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight and passenger rail, freight shipping, and miscellaneous

165

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 3 AUGUST 16, 2012  

E-Print Network [OSTI]

there is significant uncertainty in its future intensity, the current forecast is for a slowly strengthening TC which, 3) forecast output from global models, 4) the current and projected state of the Madden with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all

Gray, William

166

Electricity Demand and Energy Consumption Management System  

E-Print Network [OSTI]

This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

Sarmiento, Juan Ojeda

2008-01-01T23:59:59.000Z

167

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information ...

Choi, Han-Lim

2009-01-01T23:59:59.000Z

168

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting Seasonal Prediction · POAMA · Issues for future Outline #12;Pacific Adaptation Strategy Assistance Program Major source Adaptation Strategy Assistance Program El Nino Mean State · Easterlies westward surface current upwelling

Lim, Eun-pa

169

Wind Power Forecasting  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email List Self Supplied Balancing Reserves Dynamic...

170

Solar forecasting review  

E-Print Network [OSTI]

2.1.2 European Solar Radiation Atlas (ESRA)2.4 Evaluation of Solar Forecasting . . . . . . . . .2.4.1 Solar Variability . . . . . . . . . . . . .

Inman, Richard Headen

2012-01-01T23:59:59.000Z

171

Wind Power Forecasting  

Science Journals Connector (OSTI)

The National Center for Atmospheric Research (NCAR) has configured a Wind Power Forecasting System for Xcel Energy that integrates high resolution and ensemble...

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

172

Demand Response Programs, 6. edition  

SciTech Connect (OSTI)

The report provides a look at the past, present, and future state of the market for demand/load response based upon market price signals. It is intended to provide significant value to individuals and companies who are considering participating in demand response programs, energy providers and ISOs interested in offering demand response programs, and consultants and analysts looking for detailed information on demand response technology, applications, and participants. The report offers a look at the current Demand Response environment in the energy industry by: defining what demand response programs are; detailing the evolution of program types over the last 30 years; discussing the key drivers of current initiatives; identifying barriers and keys to success for the programs; discussing the argument against subsidization of demand response; describing the different types of programs that exist including:direct load control, interruptible load, curtailable load, time-of-use, real time pricing, and demand bidding/buyback; providing examples of the different types of programs; examining the enablers of demand response programs; and, providing a look at major demand response programs.

NONE

2007-10-15T23:59:59.000Z

173

Model documentation report: Industrial sector demand module of the National Energy Modeling System  

SciTech Connect (OSTI)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects. The NEMS Industrial Demand Model is a dynamic accounting model, bringing together the disparate industries and uses of energy in those industries, and putting them together in an understandable and cohesive framework. The Industrial Model generates mid-term (up to the year 2015) forecasts of industrial sector energy demand as a component of the NEMS integrated forecasting system. From the NEMS system, the Industrial Model receives fuel prices, employment data, and the value of industrial output. Based on the values of these variables, the Industrial Model passes back to the NEMS system estimates of consumption by fuel types.

NONE

1997-01-01T23:59:59.000Z

174

Demand Response  

Broader source: Energy.gov (indexed) [DOE]

Assessment for Eastern Interconnection Youngsun Baek, Stanton W. Hadley, Rocio Martinez, Gbadebo Oladosu, Alexander M. Smith, Fran Li, Paul Leiby and Russell Lee Prepared for FY12 DOE-CERTS Transmission Reliability R&D Internal Program Review September 20, 2012 2 Managed by UT-Battelle for the U.S. Department of Energy DOE National Laboratory Studies Funded to Support FOA 63 * DOE set aside $20 million from transmission funding for national laboratory studies. * DOE identified four areas of interest: 1. Transmission Reliability 2. Demand Side Issues 3. Water and Energy 4. Other Topics * Argonne, NREL, and ORNL support for EIPC/SSC/EISPC and the EISPC Energy Zone is funded through Area 4. * Area 2 covers LBNL and NREL work in WECC and

175

Demand Response and Open Automated Demand Response  

E-Print Network [OSTI]

LBNL-3047E Demand Response and Open Automated Demand Response Opportunities for Data Centers G described in this report was coordinated by the Demand Response Research Center and funded by the California. Demand Response and Open Automated Demand Response Opportunities for Data Centers. California Energy

176

Negotiating future climates for public policy: a critical assessment of the development of  

E-Print Network [OSTI]

) or of seasonal forecasting (a few months): Earth system models aim to simulate future climatic evolution over

Hulme, Mike

177

Technology Forecasting Scenario Development  

E-Print Network [OSTI]

Technology Forecasting and Scenario Development Newsletter No. 2 October 1998 Systems Analysis was initiated on the establishment of a new research programme entitled Technology Forecasting and Scenario and commercial applica- tion of new technology. An international Scientific Advisory Panel has been set up

178

CAPP 2010 Forecast.indd  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Forecast, Markets & Pipelines 1 Crude Oil Forecast, Markets & Pipelines June 2010 2 CANADIAN ASSOCIATION OF PETROLEUM PRODUCERS Disclaimer: This publication was prepared by the...

179

Measured energy savings and demand reduction from a reflective roof membrane on a large retail store in Austin  

E-Print Network [OSTI]

the abated annual energy and demand expenditures, simplea/c annual abated energy and demand expenditures and presentof future abated energy and demand expenditures is estimated

Konopacki, Steven J.; Akbari, Hashem

2001-01-01T23:59:59.000Z

180

Commercial & Industrial Demand Response  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Resources News & Events Expand News & Events Skip navigation links Smart Grid Demand Response Agricultural Residential Demand Response Commercial & Industrial Demand Response...

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

High Temperatures & Electricity Demand  

E-Print Network [OSTI]

High Temperatures & Electricity Demand An Assessment of Supply Adequacy in California Trends.......................................................................................................1 HIGH TEMPERATURES AND ELECTRICITY DEMAND.....................................................................................................................7 SECTION I: HIGH TEMPERATURES AND ELECTRICITY DEMAND ..........................9 BACKGROUND

182

Income distribution trends and future food demand  

Science Journals Connector (OSTI)

...available for food consumption. While there is...between food and energy markets via biofuels...century studied food consumption of the Belgian working...in the market or home-produced. Two...over time. Food consumption can be disaggregated...is poor. Their average income is Y, while...

2010-01-01T23:59:59.000Z

183

The house of the future  

ScienceCinema (OSTI)

Learn what it will take to create tomorrow's net-zero energy home as scientists reveal the secrets of cool roofs, smart windows, and computer-driven energy control systems. The net-zero energy home: Scientists are working to make tomorrow's homes more than just energy efficient -- they want them to be zero energy. Iain Walker, a scientist in the Lab's Energy Performance of Buildings Group, will discuss what it takes to develop net-zero energy houses that generate as much energy as they use through highly aggressive energy efficiency and on-site renewable energy generation. Talking back to the grid: Imagine programming your house to use less energy if the electricity grid is full or price are high. Mary Ann Piette, deputy director of Berkeley Lab's building technology department and director of the Lab's Demand Response Research Center, will discuss how new technologies are enabling buildings to listen to the grid and automatically change their thermostat settings or lighting loads, among other demands, in response to fluctuating electricity prices. The networked (and energy efficient) house: In the future, your home's lights, climate control devices, computers, windows, and appliances could be controlled via a sophisticated digital network. If it's plugged in, it'll be connected. Bruce Nordman, an energy scientist in Berkeley Lab's Energy End-Use Forecasting group, will discuss how he and other scientists are working to ensure these networks help homeowners save energy.

None

2010-09-01T23:59:59.000Z

184

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. ...

Steven T. Sonka; James W. Mjelde; Peter J. Lamb; Steven E. Hollinger; Bruce L. Dixon

1987-09-01T23:59:59.000Z

185

Comparing Forecast Skill  

Science Journals Connector (OSTI)

A basic question in forecasting is whether one prediction system is more skillful than another. Some commonly used statistical significance tests cannot answer this question correctly if the skills are computed on a common period or using a common ...

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

186

Energy in Europe: Demand, Forecast, Control and Supply  

Science Journals Connector (OSTI)

Adequate and reasonably-priced energy supplies are fundamental to the functioning of the economy and to the stability of the society of all countries. Energy questions, therefore, have become of steadily incre...

H.-F. Wagner

1981-01-01T23:59:59.000Z

187

The National Energy Modeling System: An Overview 1998 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

RESIDENTIAL DEMAND MODULE RESIDENTIAL DEMAND MODULE blueball.gif (205 bytes) Housing Stock Submodule blueball.gif (205 bytes) Appliance Stock Submodule blueball.gif (205 bytes) Technology Choice Submodule blueball.gif (205 bytes) Shell Integrity Submodule blueball.gif (205 bytes) Fuel Consumption Submodule The residential demand module (RDM) forecasts energy consumption by Census division for seven marketed energy sources plus solar thermal and geothermal energy. The RDM is a structural model and its forecasts are built up from projections of the residential housing stock and of the energy-consuming equipment contained therein. The components of the RDM and its interactions with the NEMS system are shown in Figure 5. NEMS provides forecasts of residential energy prices, population, and housing starts,

188

Transportation Demand This  

Gasoline and Diesel Fuel Update (EIA)

(VMT) per vehicle by fleet type stays constant over the forecast period based on the Oak Ridge National Laboratory fleet data. Fleet fuel economy for both conventional and...

189

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

1992-02-01T23:59:59.000Z

190

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01T23:59:59.000Z

191

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

Forecasting Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels MICHAEL YE, ∗ JOHN ZYREN, ∗∗ AND JOANNE SHORE ∗∗ Abstract This paper presents a short-term monthly forecasting model of West Texas Intermedi- ate crude oil spot price using OECD petroleum inventory levels. Theoretically, petroleum inventory levels are a measure of the balance, or imbalance, between petroleum production and demand, and thus provide a good market barometer of crude oil price change. Based on an understanding of petroleum market fundamentals and observed market behavior during the post-Gulf War period, the model was developed with the objectives of being both simple and practical, with required data readily available. As a result, the model is useful to industry and government decision-makers in forecasting price and investigat- ing the impacts of changes on price, should inventories,

192

The Role of Demand Response Policy Forum Series  

E-Print Network [OSTI]

The Role of Demand Response Policy Forum Series Beyond 33 Percent: California's Renewable Future and Demand Response #12;Historic focus on Seasonal Grid Stress PG&E Demand Bid Test Day 0 2000 4000 6000 8000 Communication Latency #12;Bottom Up Review of End-Use Loads for Demand Response 5 Commercial Residential

California at Davis, University of

193

Global food demand and the sustainable intensification of agriculture  

Science Journals Connector (OSTI)

...analyzed crop demand (utilization...ZZQQhy2007 per capita real (inflation-adjusted) GDP (Table S1...nut oil, an energy dense commodity...future crop demand that we present...nation the mean per capita crop demands...per capita GDP). Crop Demand...

David Tilman; Christian Balzer; Jason Hill; Belinda L. Befort

2011-01-01T23:59:59.000Z

194

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect (OSTI)

We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

195

Renewable Electricity Futures Study  

E-Print Network [OSTI]

Renewable Electricity Futures Study End-use Electricity Demand Volume 3 of 4 Volume 2 PDF Volume 3;Renewable Electricity Futures Study Edited By Hand, M.M. National Renewable Energy Laboratory Baldwin, S. U Sandor, D. National Renewable Energy Laboratory Suggested Citations Renewable Electricity Futures Study

196

Global Energy: Supply, Demand, Consequences, Opportunities  

SciTech Connect (OSTI)

July 29, 2008 Berkeley Lab lecture: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

Arun Majumdar

2008-08-14T23:59:59.000Z

197

Global Energy: Supply, Demand, Consequences, Opportunities  

ScienceCinema (OSTI)

July 29, 2008 Berkeley Lab lecture: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

Arun Majumdar

2010-01-08T23:59:59.000Z

198

Assumptions to the Annual Energy Outlook 2002 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 19). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated

199

A Vision of Demand Response - 2016  

SciTech Connect (OSTI)

Envision a journey about 10 years into a future where demand response is actually integrated into the policies, standards, and operating practices of electric utilities. Here's a bottom-up view of how demand response actually works, as seen through the eyes of typical customers, system operators, utilities, and regulators. (author)

Levy, Roger

2006-10-15T23:59:59.000Z

200

Advanced Demand Responsive Lighting  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Demand Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center Technical Advisory Group Meeting August 31, 2007 10:30 AM - Noon Meeting Agenda * Introductions (10 minutes) * Main Presentation (~ 1 hour) * Questions, comments from panel (15 minutes) Project History * Lighting Scoping Study (completed January 2007) - Identified potential for energy and demand savings using demand responsive lighting systems - Importance of dimming - New wireless controls technologies * Advanced Demand Responsive Lighting (commenced March 2007) Objectives * Provide up-to-date information on the reliability, predictability of dimmable lighting as a demand resource under realistic operating load conditions * Identify potential negative impacts of DR lighting on lighting quality Potential of Demand Responsive Lighting Control

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Sandia National Laboratories: solar forecasting  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

202

The National Energy Modeling System: An Overview 1998 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

COMMERCIAL DEMAND MODULE COMMERCIAL DEMAND MODULE blueball.gif (205 bytes) Floorspace Submodule blueball.gif (205 bytes) Energy Service Demand Submodule blueball.gif (205 bytes) Equipment Choice Submodule blueball.gif (205 bytes) Energy Consumption Submodule The commercial demand module (CDM) forecasts energy consumption by Census division for eight marketed energy sources plus solar thermal energy. For the three major commercial sector fuels, electricity, natural gas and distillate oil, the CDM is a "structural" model and its forecasts are built up from projections of the commercial floorspace stock and of the energy-consuming equipment contained therein. For the remaining five marketed "minor fuels," simple econometric projections are made. The commercial sector encompasses business establishments that are not

203

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

Exponential smoothing, often used in sales forecasting for inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains appropriate under more general conditions, where the variance is allowed to grow or contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular, the problem of finding the predictive distribution of aggregate lead-time demand, for use in inventory control calculations, is considered using a bootstrap approach. A method for establishing order-up-to levels directly from the simulated predictive distribution is also explored.

Ralph D. Snyder; Anne B. Koehler; J.Keith Ord

2002-01-01T23:59:59.000Z

204

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

Rate Forecasts 19 5. EIA Forecast: Regional Coal Production 22 6. Wood Mackenzie Forecast: W.V. Steam to data currently published by the Energy Information Administration (EIA), coal production in the state in this report calls for state production to decline by 11.3 percent in 2009 to 140.2 million tons. During

Mohaghegh, Shahab

205

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Table 2. Total Energy Consumption, Actual vs. Forecasts Table 3. Total Petroleum Consumption, Actual vs. Forecasts Table 4. Total Natural Gas Consumption, Actual vs. Forecasts Table 5. Total Coal Consumption, Actual vs. Forecasts Table 6. Total Electricity Sales, Actual vs. Forecasts Table 7. Crude Oil Production, Actual vs. Forecasts Table 8. Natural Gas Production, Actual vs. Forecasts Table 9. Coal Production, Actual vs. Forecasts Table 10. Net Petroleum Imports, Actual vs. Forecasts Table 11. Net Natural Gas Imports, Actual vs. Forecasts Table 12. Net Coal Exports, Actual vs. Forecasts Table 13. World Oil Prices, Actual vs. Forecasts Table 14. Natural Gas Wellhead Prices, Actual vs. Forecasts Table 15. Coal Prices to Electric Utilities, Actual vs. Forecasts

206

On Sequential Probability Forecasting  

E-Print Network [OSTI]

at the same time. [Probability, Statistics and Truth, MacMillan 1957. page 11] ... the collective "denotes a collective wherein the attribute of the single event is the number of points thrown. [Probability, StatisticsOn Sequential Probability Forecasting David A. Bessler 1 David A. Bessler Texas A&M University

McCarl, Bruce A.

207

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production.

Jianzhou Wang; Yao Dong; Jie Wu; Ren Mu; He Jiang

2011-01-01T23:59:59.000Z

208

Energy demand simulation for East European countries  

Science Journals Connector (OSTI)

The analysis and created statistical models of energy consumption tendencies in the European Union (EU25), including new countries in transition, are presented. The EU15 market economy countries and countries in transition are classified into six clusters by relative indicators of Gross Domestic Product (GDP/P) and energy demand (W/P) per capita. The specified statistical models of energy intensity W/GDP non-linear stochastic tendencies have been discovered with respect to the clusters of classified countries. The new energy demand simulation models have been developed for the demand management in timeâ??territory hierarchy in various scenarios of short-term and long-term perspective on the basis of comparative analysis methodology. The non-linear statistical models were modified to GDP, W/P and electricity (E/P) final consumption long-term forecasts for new associated East European countries and, as an example, for the Baltic Countries, including Lithuania.

Jonas Algirdas Kugelevicius; Algirdas Kuprys; Jonas Kugelevicius

2007-01-01T23:59:59.000Z

209

Addressing Energy Demand through Demand Response: International Experiences and Practices  

E-Print Network [OSTI]

Addressing Energy Demand through Demand Response:both the avoided energy costs (and demand charges) as wellCoordination of Energy Efficiency and Demand Response,

Shen, Bo

2013-01-01T23:59:59.000Z

210

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sánchez The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors,

211

FORSITE: a geothermal site development forecasting system  

SciTech Connect (OSTI)

The Geothermal Site Development Forecasting System (FORSITE) is a computer-based system being developed to assist DOE geothermal program managers in monitoring the progress of multiple geothermal electric exploration and construction projects. The system will combine conceptual development schedules with site-specific status data to predict a time-phased sequence of development likely to occur at specific geothermal sites. Forecasting includes estimation of industry costs and federal manpower requirements across sites on a year-by-year basis. The main advantage of the system, which relies on reporting of major, easily detectable industry activities, is its ability to use relatively sparse data to achieve a representation of status and future development.

Entingh, D.J.; Gerstein, R.E.; Kenkeremath, L.D.; Ko, S.M.

1981-10-01T23:59:59.000Z

212

Long-term Industrial Energy Forecasting (LIEF) model (18-sector version)  

SciTech Connect (OSTI)

The new 18-sector Long-term Industrial Energy Forecasting (LIEF) model is designed for convenient study of future industrial energy consumption, taking into account the composition of production, energy prices, and certain kinds of policy initiatives. Electricity and aggregate fossil fuels are modeled. Changes in energy intensity in each sector are driven by autonomous technological improvement (price-independent trend), the opportunity for energy-price-sensitive improvements, energy price expectations, and investment behavior. Although this decision-making framework involves more variables than the simplest econometric models, it enables direct comparison of an econometric approach with conservation supply curves from detailed engineering analysis. It also permits explicit consideration of a variety of policy approaches other than price manipulation. The model is tested in terms of historical data for nine manufacturing sectors, and parameters are determined for forecasting purposes. Relatively uniform and satisfactory parameters are obtained from this analysis. In this report, LIEF is also applied to create base-case and demand-side management scenarios to briefly illustrate modeling procedures and outputs.

Ross, M.H. [Univ. of Michigan, Ann Arbor, MI (US). Dept. of Physics; Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. [Argonne National Lab., IL (US)

1993-05-01T23:59:59.000Z

213

Demand Response Valuation Frameworks Paper  

E-Print Network [OSTI]

benefits of Demand Side Management (DSM) are insufficient toefficiency, demand side management (DSM) cost effectivenessResearch Center Demand Side Management Demand Side Resources

Heffner, Grayson

2010-01-01T23:59:59.000Z

214

Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns  

Science Journals Connector (OSTI)

This research uses a Self-Organizing Map neural network model (SOM) as a short-term forecasting method. The objective is to obtain the demand curve of certain hours of the next day. In order to validate the model...

C. Senabre; S. Valero; J. Aparicio

2010-01-01T23:59:59.000Z

215

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

Various factors influence the development of the voluntary 'green' power market--the market in which consumers purchase or produce power from non-polluting, renewable energy sources. These factors include climate policies, renewable portfolio standards (RPS), renewable energy prices, consumers' interest in purchasing green power, and utilities' interest in promoting existing programs and in offering new green options. This report presents estimates of voluntary market demand for green power through 2015 that were made using historical data and three scenarios: low-growth, high-growth, and negative-policy impacts. The resulting forecast projects the total voluntary demand for renewable energy in 2015 to range from 63 million MWh annually in the low case scenario to 157 million MWh annually in the high case scenario, representing an approximately 2.5-fold difference. The negative-policy impacts scenario reflects a market size of 24 million MWh. Several key uncertainties affect the results of this forecast, including uncertainties related to growth assumptions, the impacts that policy may have on the market, the price and competitiveness of renewable generation, and the level of interest that utilities have in offering and promoting green power products.

Bird, L.; Holt, E.; Sumner, J.; Kreycik, C.

2010-05-01T23:59:59.000Z

216

Mass Market Demand Response and Variable Generation Integration Issues: A  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Mass Market Demand Response and Variable Generation Integration Issues: A Mass Market Demand Response and Variable Generation Integration Issues: A Scoping Study Title Mass Market Demand Response and Variable Generation Integration Issues: A Scoping Study Publication Type Report Refereed Designation Unknown Year of Publication 2011 Authors Cappers, Peter, Andrew D. Mills, Charles A. Goldman, Ryan H. Wiser, and Joseph H. Eto Pagination 76 Date Published 10/2011 Publisher LBNL City Berkeley Keywords demand response, electricity markets and policy group, energy analysis and environmental impacts department, renewable generation integration, smart grid Abstract The penetration of renewable generation technology (e.g., wind, solar) is expected to dramatically increase in the United States during the coming years as many states are implementing policies to expand this sector through regulation and/or legislation. It is widely understood, though, that large scale deployment of certain renewable energy sources, namely wind and solar, poses system integration challenges because of its variable and often times unpredictable production characteristics (NERC, 2009). Strategies that rely on existing thermal generation resources and improved wind and solar energy production forecasts to manage this variability are currently employed by bulk power system operators, although a host of additional options are envisioned for the near future. Demand response (DR), when properly designed, could be a viable resource for managing many of the system balancing issues associated with integrating large-scale variable generation (VG) resources (NERC, 2009). However, demand-side options would need to compete against strategies already in use or contemplated for the future to integrate larger volumes of wind and solar generation resources. Proponents of smart grid (of which Advanced Metering Infrastructure or AMI is an integral component) assert that the technologies associated with this new investment can facilitate synergies and linkages between demand-side management and bulk power system needs. For example, smart grid proponents assert that system-wide implementation of advanced metering to mass market customers (i.e., residential and small commercial customers) as part of a smart grid deployment enables a significant increase in demand response capability.1 Specifically, the implementation of AMI allows electricity consumption information to be captured, stored and utilized at a highly granular level (e.g., 15-60 minute intervals in most cases) and provides an opportunity for utilities and public policymakers to more fully engage electricity customers in better managing their own usage through time-based rates and near-real time feedback to customers on their usage patterns while also potentially improving the management of the bulk power system. At present, development of time-based rates and demand response programs and the installation of variable generation resources are moving forward largely independent of each other in state and regional regulatory and policy forums and without much regard to the complementary nature of their operational characteristics.2 By 2020, the electric power sector is expected to add ~65 million advanced meters3 (which would reach ~47% of U.S. households) as part of smart grid and AMI4 deployments (IEE, 2010) and add ~40-80 GW of wind and solar capacity (EIA, 2010). Thus, in this scoping study, we focus on a key question posed by policymakers: what role can the smart grid (and its associated enabling technology) play over the next 5-10 years in helping to integrate greater penetration of variable generation resources by providing mass market customers with greater access to demand response opportunities? There is a well-established body of research that examines variable generation integration issues as well as demand response potential, but the nexus between the two has been somewhat neglected by the industry. The studies that have been conducted are informative concerning what could be accomplished with strong broad-based support for the expansion of demand response opportunities, but typically do not discuss the many barriers that stand in the way of reaching this potential. This study examines how demand side resources could be used to integrate wind and solar resources in the bulk power system, identifies barriers that currently limit the use of demand side strategies, and suggests several factors that should be considered in assessing alternative strategies that can be employed to integrate wind and solar resources in the bulk power system. It is difficult to properly gauge the role that DR could play in managing VG integration issues in the near future without acknowledging and understanding the entities and institutions that govern the interactions between variable generation and mass market customers (see Figure ES-1). Retail entities, like load-serving entities (LSE) and aggregators of retail customers (ARC), harness the demand response opportunities of mass market customers through tariffs (and DR programs) that are approved by state regulatory agencies or local governing entities (in the case of public power). The changes in electricity consumption induced by DR as well as the changes in electricity production due to the variable nature of wind and solar generation technologies is jointly managed by bulk power system operators. Bulk power system operators function under tariffs approved by the Federal Energy Regulatory Commission (FERC) and must operate their systems in accordance with rules set by regional reliability councils. These reliability rules are derived from enforceable standards that are set by the North American Electric Reliability Corporation (NERC) and approved by federal regulators. Thus, the role that DR can play in managing VG integration issues is contingent on what opportunities state and local regulators are willing to approve and how customers' response to the DR opportunities can be integrated into the bulk power system both electrically (due to reliability rules) and financially (due to market rules).

217

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sanchez by Esmeralda Sanchez Errata -(7/14/04) The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors, expressed as the percentage difference between the Reference Case projection and actual historic value, shown for every AEO and for each year in the forecast horizon (for a given variable). The historical data are typically taken from the Annual Energy Review (AER). The last column of Table 1 provides a summary of the most recent average absolute forecast errors. The calculation of the forecast error is shown in more detail in Tables 2 through 18. Because data for coal prices to electric generating plants were not available from the AER, data from the Monthly Energy Review (MER), July 2003 were used.

218

Chinese Oil Demand: Steep Incline Ahead  

U.S. Energy Information Administration (EIA) Indexed Site

Chinese Oil Demand: Chinese Oil Demand: Steep Incline Ahead Malcolm Shealy Alacritas, Inc. April 7, 2008 Oil Demand: China, India, Japan, South Korea 0 2 4 6 8 1995 2000 2005 2010 Million Barrels/Day China South Korea Japan India IEA China Oil Forecast 0 2 4 6 8 10 12 14 16 18 2000 2005 2010 2015 2020 2025 2030 Million Barrels/Day WEO 2007 16.3 mbd 12.7 mbd IEA China Oil Forecasts 0 2 4 6 8 10 12 14 16 18 2000 2005 2010 2015 2020 2025 2030 Million Barrels/Day WEO 2007 WEO 2006 WEO 2004 WEO 2002 Vehicle Sales in China 0 2 4 6 8 10 1990 1995 2000 2005 2010 Million Vehicles/Year Vehicle Registrations in China 0 10 20 30 40 50 1990 1995 2000 2005 2010 Million Vehicles/Year Vehicle Density vs GDP per Capita 0 20 40 60 80 100 120 140 160 180 200 0 4,000 8,000 12,000 16,000 GDP per capita, 2005$ PPP Vehicles per thousand people Taiwan South Korea China Vehicle Density vs GDP per Capita

219

On the stock control performance of intermittent demand estimators  

Science Journals Connector (OSTI)

The purpose of this paper is to assess the empirical stock control performance of intermittent demand estimation procedures. The forecasting methods considered are the simple moving average, single exponential smoothing, Croston's method and a new method recently developed by the authors of this paper. We first discuss the nature of the empirical demand data set (3000 stock keeping units) and we specify the stock control model to be used for experimentation purposes. Performance measures are then selected to report customer service level and stock volume differences. The out-of-sample empirical comparison results demonstrate the superior stock control performance of the new intermittent demand forecasting method and enable insights to be gained into the empirical utility of the other estimators.

Aris A. Syntetos; John E. Boylan

2006-01-01T23:59:59.000Z

220

Mass Market Demand Response  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Mass Market Demand Response Mass Market Demand Response Speaker(s): Karen Herter Date: July 24, 2002 - 12:00pm Location: Bldg. 90 Demand response programs are often quickly and poorly crafted in reaction to an energy crisis and disappear once the crisis subsides, ensuring that the electricity system will be unprepared when the next crisis hits. In this paper, we propose to eliminate the event-driven nature of demand response programs by considering demand responsiveness a component of the utility obligation to serve. As such, demand response can be required as a condition of service, and the offering of demand response rates becomes a requirement of utilities as an element of customer service. Using this foundation, we explore the costs and benefits of a smart thermostat-based demand response system capable of two types of programs: (1) a mandatory,

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network [OSTI]

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts Antonio that the inherent variability in wind power generation and the related difficulty in predicting future generation profiles, raise major challenges to wind power integration into the electricity grid. In this work we study

Giannitrapani, Antonello

222

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS  

E-Print Network [OSTI]

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg.girodo@uni-oldenburg.de ABSTRACT Solar energy is expected to contribute major shares of the future global energy supply. Due to its and solar energy conversion processes has to account for this behaviour in respective operating strategies

Heinemann, Detlev

223

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model conditioning (HVAC) systems. In particular, if occupancy can be accurately pre- dicted, HVAC systems can potentially be controlled to op- erate more efficiently. For example, an HVAC system can pre-heat or pre

Hoff, William A.

224

Introduction An important goal in operational weather forecasting  

E-Print Network [OSTI]

sensitive areas. To answer these questions simulation experiments with state-of-the-art numerical weather prediction (NWP) models have proved great value to test future meteorological observing systems a priori102 Introduction An important goal in operational weather forecasting is to reduce the number

Haak, Hein

225

Journey data based arrival forecasting for bicycle hire schemes  

E-Print Network [OSTI]

Journey data based arrival forecasting for bicycle hire schemes Marcel C. Guenther and Jeremy T. The global emergence of city bicycle hire schemes has re- cently received a lot of attention of future bicycle migration trends, as these assist service providers to ensure availability of bicycles

Imperial College, London

226

Demand Response Assessment INTRODUCTION  

E-Print Network [OSTI]

Demand Response Assessment INTRODUCTION This appendix provides more detail on some of the topics raised in Chapter 4, "Demand Response" of the body of the Plan. These topics include 1. The features, advantages and disadvantages of the main options for stimulating demand response (price mechanisms

227

Price forecasting for notebook computers.  

E-Print Network [OSTI]

??This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a… (more)

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

228

Ensemble Forecasts and their Verification  

E-Print Network [OSTI]

· Ensemble forecast verification ­ Performance metrics: Brier Score, CRPSS · New concepts and developments of weather Sources: Insufficient spatial resolution, truncation errors in the dynamical equations

Maryland at College Park, University of

229

Probabilistic manpower forecasting  

E-Print Network [OSTI]

- ing E. Results- Probabilistic Forecasting . 26 27 Z8 29 31 35 36 38 39 IV. CONCLUSIONS. V. GLOSSARY 42 44 APPENDICES REFERENCES 50 70 LIST OF TABLES Table Page Outline of Job-Probability Matrix Job-Probability Matrix. Possible... Outcomes of Job A Possible Outcomes of Jobs A and B 10 Possible Outcomes of Jobs A, B and C II LIST GF FIGURES Figure Page Binary Representation of Numbers 0 Through 7 12 First Cumulative Probability Table 14 3. Graph of Cumulative Probability vs...

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

230

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

This paper reports on the development of a diagnostic approach that can be used to examine the sources of numerical model forecast error that contribute to degraded tropical cyclone (TC) motion forecasts. Tropical cyclone motion forecasts depend ...

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

231

Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint  

SciTech Connect (OSTI)

Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

Zhang, J.; Hodge, B. M.; Florita, A.

2013-05-01T23:59:59.000Z

232

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

V. Shrivastava; R.B. Misra; R.C. Bansal

2012-01-01T23:59:59.000Z

233

Future Climate Analysis  

SciTech Connect (OSTI)

This report documents an analysis that was performed to estimate climatic variables for the next 10,000 years by forecasting the timing and nature of climate change at Yucca Mountain, Nevada, the site of a repository for spent nuclear fuel and high-level radioactive waste. The future-climate estimates are based on an analysis of past-climate data from analog meteorological stations, and this report provides the rationale for the selection of these analog stations. The stations selected provide an upper and a lower climate bound for each future climate, and the data from those sites will provide input to the following reports: ''Simulation of Net Infiltration for Present-Day and Potential Future Climates'' (BSC 2004 [DIRS 170007]), ''Total System Performance Assessment (TSPA) Model/Analysis for the License Application'' (BSC 2004 [DIRS 168504]), ''Features, Events, and Processes in UZ Flow and Transport'' (BSC 2004 [DIRS 170012]), and ''Features, Events, and Processes in SZ Flow and Transport'' (BSC 2004 [DIRS 170013]). Forecasting long-term future climates, especially for the next 10,000 years, is highly speculative and rarely attempted. A very limited literature exists concerning the subject, largely from the British radioactive waste disposal effort. The discussion presented here is one available forecasting method for establishing upper and lower bounds for future climate estimates. The selection of different methods is directly dependent on the available evidence used to build a forecasting argument. The method used here involves selecting a particular past climate from many past climates, as an analog for future climate. While alternative analyses are possible for the case presented for Yucca Mountain, the evidence (data) used would be the same and the conclusions would not be expected to drastically change. Other studies might develop a different rationale or select other past climates resulting in a different future climate analog. Other alternative approaches could include simulation of climate over the 10,000-year period; however, this modeling extrapolation is well beyond the bounds of current scientific practice and would not provide results with better confidence. A corroborative alternative approach may be found in ''Future Climate Analysis-10,000 Years to 1,000,000 Years After Present'' (Sharpe 2003 [DIRS 161591]). The current revision of this report is prepared in accordance with ''Technical Work Plan for: Unsaturated Zone Flow Analysis and Model Report Integration'' (BSC 2004 [DIRS 169654]).

C. G. Cambell

2004-09-03T23:59:59.000Z

234

Uranium 2009 resources, production and demand  

E-Print Network [OSTI]

With several countries currently building nuclear power plants and planning the construction of more to meet long-term increases in electricity demand, uranium resources, production and demand remain topics of notable interest. In response to the projected growth in demand for uranium and declining inventories, the uranium industry – the first critical link in the fuel supply chain for nuclear reactors – is boosting production and developing plans for further increases in the near future. Strong market conditions will, however, be necessary to trigger the investments required to meet projected demand. The "Red Book", jointly prepared by the OECD Nuclear Energy Agency and the International Atomic Energy Agency, is a recognised world reference on uranium. It is based on information compiled in 40 countries, including those that are major producers and consumers of uranium. This 23rd edition provides a comprehensive review of world uranium supply and demand as of 1 January 2009, as well as data on global ur...

Organisation for Economic Cooperation and Development. Paris

2010-01-01T23:59:59.000Z

235

Project Profile: Forecasting and Influencing Technological Progress...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of...

236

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

Much of product level forecasting is based upon time series techniques. However, traditional time series forecasting techniques have offered either smoothing constant adaptability or consideration of various t...

John T. Mentzer Ph.D.

237

Electricity price forecasting in a grid environment.  

E-Print Network [OSTI]

??Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate… (more)

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

238

Energy Department Forecasts Geothermal Achievements in 2015 ...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in...

239

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation by Susan H. Holte In this paper, the Office of Integrated Analysis and Forecasting (OIAF) of the Energy Information Administration (EIA) evaluates the projections published in the Annual Energy Outlook (AEO), (1) by comparing the projections from the Annual Energy Outlook 1982 through the Annual Energy Outlook 2001 with actual historical values. A set of major consumption, production, net import, price, economic, and carbon dioxide emissions variables are included in the evaluation, updating similar papers from previous years. These evaluations also present the reasons and rationales for significant differences. The Office of Integrated Analysis and Forecasting has been providing an

240

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Title of Paper Annual Energy Outlook Forecast Evaluation Title of Paper Annual Energy Outlook Forecast Evaluation by Susan H. Holte OIAF has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Natural gas has generally been the fuel with the least accurate forecasts of consumption, production, and prices. Natural gas was the last fossil fuel to be deregulated following the strong regulation of energy markets in the 1970s and early 1980s. Even after deregulation, the behavior

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Demand response enabling technology development  

E-Print Network [OSTI]

Demand Response Enabling Technology Development Phase IEfficiency and Demand Response Programs for 2005/2006,Application to Demand Response Energy Pricing” SenSys 2003,

2006-01-01T23:59:59.000Z

242

Demand Response Spinning Reserve Demonstration  

E-Print Network [OSTI]

F) Enhanced ACP Date RAA ACP Demand Response – SpinningReserve Demonstration Demand Response – Spinning Reservesupply spinning reserve. Demand Response – Spinning Reserve

2007-01-01T23:59:59.000Z

243

Cross-sector Demand Response  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Resources News & Events Expand News & Events Skip navigation links Smart Grid Demand Response Agricultural Residential Demand Response Commercial & Industrial Demand Response...

244

Demand Response Programs for Oregon  

E-Print Network [OSTI]

Demand Response Programs for Oregon Utilities Public Utility Commission May 2003 Public Utility ....................................................................................................................... 1 Types of Demand Response Programs............................................................................ 3 Demand Response Programs in Oregon

245

Automated Demand Response and Commissioning  

E-Print Network [OSTI]

Fully-Automated Demand Response Test in Large Facilities14in DR systems. Demand Response using HVAC in Commercialof Fully Automated Demand Response in Large Facilities”

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-01-01T23:59:59.000Z

246

Demand Response In California  

Broader source: Energy.gov [DOE]

Presentation covers the demand response in California and is given at the FUPWG 2006 Fall meeting, held on November 1-2, 2006 in San Francisco, California.

247

Water Requirements for Future Energy production in California  

E-Print Network [OSTI]

than of future power cooling in 1975. WATER REQUIREMENTSFORprograms for power currently cooling carried in the future.uses. Demand for power plant cooling constitutes a very

Sathaye, J.A.

2011-01-01T23:59:59.000Z

248

Energy technologies and their impact on demand  

SciTech Connect (OSTI)

Despite the uncertainties, energy demand forecasts must be made to guide government policies and public and private-sector capital investment programs. Three principles can be identified in considering long-term energy prospects. First energy demand will continue to grow, driven by population growth, economic development, and the current low per capita energy consumption in developing countries. Second, energy technology advancements alone will not solve the problem. Energy-efficient technologies, renewable resource technologies, and advanced electric power technologies will all play a major role but will not be able to keep up with the growth in world energy demand. Third, environmental concerns will limit the energy technology choices. Increasing concern for environmental protection around the world will restrict primarily large, centralized energy supply facilities. The conclusion is that energy system diversity is the only solution. The energy system must be planned with consideration of both supply and demand technologies, must not rely on a single source of energy, must take advantage of all available technologies that are specially suited to unique local conditions, must be built with long-term perspectives, and must be able to adapt to change.

Drucker, H.

1995-06-01T23:59:59.000Z

249

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Electricity Electricity Electricity consumption nearly doubles in the IEO2005 projection period. The emerging economies of Asia are expected to lead the increase in world electricity use. Figure 58. World Net Electricity Consumption, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 59. World Net Electricity Consumption by Region, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data The International Energy Outlook 2005 (IEO2005) reference case projects that world net electricity consumption will nearly double over the next two decades.10 Over the forecast period, world electricity demand is projected to grow at an average rate of 2.6 percent per year, from 14,275 billion

250

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Natural Gas Natural gas is the fastest growing primary energy source in the IEO2005 forecast. Consumption of natural gas is projected to increase by nearly 70 percent between 2002 and 2025, with the most robust growth in demand expected among the emerging economies. Figure 34. World Natural Gas Consumption, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center on 202-586-8800. Figure Data Figure 35. Natural Gas Consumption by Region, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 36. Increase in Natural Gas Consumption by Region and Country, 2002-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data

251

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

1998 With Projections to 2020 1998 With Projections to 2020 Annual Energy Outlook 1999 Report will be Available on December 9, 1998 Preface The Annual Energy Outlook 1998 (AEO98) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO98 reference case. The next section, “Legislation and Regulations,” describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. “Issues in Focus” discusses three current energy issues—electricity restructuring, renewable portfolio standards, and carbon emissions. It is followed by the analysis

252

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not ... Keywords: Artificial neural networks hybrid systems, Linear combination of forecasts, Maximum likelihood estimation, Time series forecasters, Unbiased forecasters

Paulo Renato A. Firmino; Paulo S. G. De Mattos Neto; Tiago A. E. Ferreira

2014-02-01T23:59:59.000Z

253

NOAA Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps  

E-Print Network [OSTI]

Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12;Bay Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12 N Collier N Charlotte S Charlotte NOAA Harmful Algal Bloom Operational Forecast System Southwest

254

Future Climate Analysis  

SciTech Connect (OSTI)

This Analysis/Model Report (AMR) documents an analysis that was performed to estimate climatic variables for the next 10,000 years by forecasting the timing and nature of climate change at Yucca Mountain (YM), Nevada (Figure 1), the site of a potential repository for high-level radioactive waste. The future-climate estimates are based on an analysis of past-climate data from analog meteorological stations, and this AMR provides the rationale for the selection of these analog stations. The stations selected provide an upper and a lower climate bound for each future climate, and the data from those sites will provide input to the infiltration model (USGS 2000) and for the total system performance assessment for the Site Recommendation (TSPA-SR) at YM. Forecasting long-term future climates, especially for the next 10,000 years, is highly speculative and rarely attempted. A very limited literature exists concerning the subject, largely from the British radioactive waste disposal effort. The discussion presented here is one method, among many, of establishing upper and lower bounds for future climate estimates. The method used here involves selecting a particular past climate from many past climates, as an analog for future climate. Other studies might develop a different rationale or select other past climates resulting in a different future climate analog. Revision 00 of this AMR was prepared in accordance with the ''Work Direction and Planning Document for Future Climate Analysis'' (Peterman 1999) under Interagency Agreement DE-AI08-97NV12033 with the U.S. Department of Energy (DOE). The planning document for the technical scope, content, and management of ICN 01 of this AMR is the ''Technical Work Plan for Unsaturated Zone (UZ) Flow and Transport Process Model Report'' (BSC 2001a). The scope for the TBV resolution actions in this ICN is described in the ''Technical Work Plan for: Integrated Management of Technical Product Input Department''. (BSC 2001b, Addendum B, Section 4.1).

James Houseworth

2001-10-12T23:59:59.000Z

255

Current Status and Potential Future Developments for  

E-Print Network [OSTI]

in response to public concern about tropical deforestation and demand for an international woodCurrent Status and Potential Future Developments for Forest Certification Richard P. Vlosky

256

EIA-Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2007 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS25 data.

257

Electricity Distribution Networks: Investment and Regulation, and Uncertain Demand  

E-Print Network [OSTI]

by the Department of Energy and Climate Change (DEEC) on an annual basis.6 5 Engineering Technical Report 115 (1988). 6 DECC Sub-national energy consumption statistics (http://www.decc.gov.uk/en/content... of non-domestic activity, which must be taken into account whilst forecasting non-domestic demand. 8 DECC Sub-national energy consumption statistics (http://www.decc.gov.uk/en/content...

Jamasb, Tooraj; Marantes, Cristiano

2011-01-31T23:59:59.000Z

258

Economy key to 1992 U. S. oil, gas demand  

SciTech Connect (OSTI)

This paper provides a forecast US oil and gas markets and industry in 1992. An end to economic recession in the U.S. will boost petroleum demand modestly in 1992 after 2 years of decline. U.S. production will resume its slide after a fractional increase in 1991. Drilling in the U.S. will set a record low. Worldwide, the key questions are economic growth and export volumes from Iraq, Kuwait, and former Soviet republics.

Beck, R.J.

1992-01-27T23:59:59.000Z

259

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

Forecast Energy Forecast Energy Jump to: navigation, search Name Forecast Energy Address 2320 Marinship Way, Suite 300 Place Sausalito, California Zip 94965 Sector Services Product Intelligent Monitoring and Forecasting Services Year founded 2010 Number of employees 11-50 Company Type For profit Website http://www.forecastenergy.net Coordinates 37.865647°, -122.496315° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.865647,"lon":-122.496315,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

260

Price forecasting for notebook computers  

E-Print Network [OSTI]

This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a series of time periods, and the rates of change in the influence...

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

...Forrest Forecasting phenology under global warming Ines Ibanez 1 * Richard B. Primack...and site-specific responses to global warming. We found that for most species...climate change|East Asia, global warming|growing season, hierarchical...

2010-01-01T23:59:59.000Z

262

Use of wind power forecasting in operational decisions.  

SciTech Connect (OSTI)

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V. (Decision and Information Sciences); (INESC Porto)

2011-11-29T23:59:59.000Z

263

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA

264

Assumptions to the Annual Energy Outlook 2001 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for

265

Assumptions to the Annual Energy Outlook 2002 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for

266

Weather forecasting : the next generation : the potential use and implementation of ensemble forecasting  

E-Print Network [OSTI]

This thesis discusses ensemble forecasting, a promising new weather forecasting technique, from various viewpoints relating not only to its meteorological aspects but also to its user and policy aspects. Ensemble forecasting ...

Goto, Susumu

2007-01-01T23:59:59.000Z

267

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Demand Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Demand Module calculates energy consumption for the four Census Regions (see Figure 5) and disaggregates the energy consumption

268

demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

269

RTP Customer Demand Response  

Science Journals Connector (OSTI)

This paper provides new evidence on customer demand response to hourly pricing from the largest and...real-time pricing...(RTP) program in the United States. RTP creates value by inducing load reductions at times...

Steven Braithwait; Michael O’Sheasy

2002-01-01T23:59:59.000Z

270

Transportation Demand This  

U.S. Energy Information Administration (EIA) Indexed Site

Transportation Demand Transportation Demand This page inTenTionally lefT blank 75 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Transportation Demand Module The NEMS Transportation Demand Module estimates transportation energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific and associated technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), buses, freight and passenger aircraft, freight

271

Wind Forecast Improvement Project Southern Study Area Final Report...  

Office of Environmental Management (EM)

Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern...

272

U.S. Department of Energy Workshop Report: Solar Resources and Forecasting  

SciTech Connect (OSTI)

This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

Stoffel, T.

2012-06-01T23:59:59.000Z

273

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network [OSTI]

shift in the short-run price elasticity of gasoline demand.A meta-analysis of the price elasticity of gasoline demand.2007. Consumer demand un- der price uncertainty: Empirical

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

274

Demand and Price Volatility: Rational Habits in International Gasoline Demand  

E-Print Network [OSTI]

analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.World crude oil and natural gas: a demand and supply model.

Scott, K. Rebecca

2011-01-01T23:59:59.000Z

275

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network [OSTI]

analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.World crude oil and natural gas: a demand and supply model.

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

276

Selling Hedge with Futures  

E-Print Network [OSTI]

trades price risk for basis risk. Once more, the basis forecast is a key to hedging with futures. Did Bill receive $5.60 per bushel for his en- tire crop? The answer depends on the quantity produced. If he produced his historical average of 24...,000 bushels, he was protected at $5.60 per bushel for the 15,000 bushels he hedged and received a price at harvest of $5.40 per bushel for the unhedged 9,000 bushels. This yields a weight- ed average price of $5.525 per bushel. Had he produced more than...

Kastens, Terry L.; Welch, Mark

2009-01-07T23:59:59.000Z

277

Energy Demand and Emission from Transport Sector in China  

Science Journals Connector (OSTI)

This paper aims to present a comprehensive overview of the current status and future trends of energy demand and emissions from transportation sector in China. ... a brief review of the national profile of energy

Yin Huang; Mengjun Wang

2013-01-01T23:59:59.000Z

278

Impact of forecasting error on the performance of capacitated multi-item production systems  

E-Print Network [OSTI]

Impact of forecasting error on the performance of capacitated multi-item production systems Jinxing multi-item production system under demand uncertainty and a rolling time horizon. The output from parameters, thus improving the performance of production systems. q 2004 Elsevier Ltd. All rights reserved

Xie, Jinxing

279

most are government agencies --local, national and international. A ten-year industry forecast put together  

E-Print Network [OSTI]

most are government agencies -- local, national and international. A ten-year industry forecast put environmental, civil government, defence and security, and transportation as the most active market segments combine geographic information systems with satellite data are in demand in a variety of disciplines

Wisconsin at Madison, University of

280

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

Solar Energy Market Forecast Solar Energy Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast Agency/Company /Organization: United States Department of Energy Sector: Energy Focus Area: Solar Topics: Market analysis, Technology characterizations Resource Type: Publications Website: giffords.house.gov/DOE%20Perspective%20on%20Solar%20Market%20Evolution References: Solar Energy Market Forecast[1] Summary " Energy markets / forecasts DOE Solar America Initiative overview Capital market investments in solar Solar photovoltaic (PV) sector overview PV prices and costs PV market evolution Market evolution considerations Balance of system costs Silicon 'normalization' Solar system value drivers Solar market forecast Additional resources"

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Changing Energy Demand Behavior: Potential of Demand-Side Management  

Science Journals Connector (OSTI)

There is a great theoretical potential to save resources by managing our demand for energy. However, demand-side management (DSM) programs targeting behavioral patterns of...

Dr. Sylvia Breukers; Dr. Ruth Mourik…

2013-01-01T23:59:59.000Z

282

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End-Use Sector Energy Consumption by End-Use Sector In the IEO2005 projections, end-use energy consumption in the residential, commercial, industrial, and transportation sectors varies widely among regions and from country to country. One way of looking at the future of world energy markets is to consider trends in energy consumption at the end-use sector level. With the exception of the transportation sector, which is almost universally dominated by petroleum products at present, the mix of energy use in the residential, commercial, and industrial sectors can vary widely from country to country, depending on a combination of regional factors, such as the availability of energy resources, the level of economic development, and political, social, and demographic factors. This chapter outlines the International Energy Outlook 2005 (IEO2005) forecast for regional energy consumption by end-use sector.

283

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Oil Markets Oil Markets IEO2005 projects that world crude oil prices in real 2003 dollars will decline from their current level by 2010, then rise gradually through 2025. In the International Energy Outlook 2005 (IEO2005) reference case, world demand for crude oil grows from 78 million barrels per day in 2002 to 103 million barrels per day in 2015 and to just over 119 million barrels per day in 2025. Much of the growth in oil consumption is projected for the emerging Asian nations, where strong economic growth results in a robust increase in oil demand. Emerging Asia (including China and India) accounts for 45 percent of the total world increase in oil use over the forecast period in the IEO2005 reference case. The projected increase in world oil demand would require an increment to world production capability of more than 42 million barrels per day relative to the 2002 crude oil production capacity of 80.0 million barrels per day. Producers in the Organization of Petroleum Exporting Countries (OPEC) are expected to be the major source of production increases. In addition, non-OPEC supply is expected to remain highly competitive, with major increments to supply coming from offshore resources, especially in the Caspian Basin, Latin America, and deepwater West Africa. The estimates of incremental production are based on current proved reserves and a country-by-country assessment of ultimately recoverable petroleum. In the IEO2005 oil price cases, the substantial investment capital required to produce the incremental volumes is assumed to exist, and the investors are expected to receive at least a 10-percent return on investment.

284

Demand Response Valuation Frameworks Paper  

E-Print Network [OSTI]

No. ER06-615-000 CAISO Demand Response Resource User Guide -8 2.1. Demand Response Provides a Range of Benefits to8 2.2. Demand Response Benefits can be Quantified in Several

Heffner, Grayson

2010-01-01T23:59:59.000Z

285

Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

286

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for ...

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

287

Demand Response In California  

Broader source: Energy.gov (indexed) [DOE]

Energy Efficiency & Energy Efficiency & Demand Response Programs Dian M. Grueneich, Commissioner Dian M. Grueneich, Commissioner California Public Utilities Commission California Public Utilities Commission FUPWG 2006 Fall Meeting November 2, 2006 Commissioner Dian M. Grueneich November 2, 2006 1 Highest Priority Resource Energy Efficiency is California's highest priority resource to: Meet energy needs in a low cost manner Aggressively reduce GHG emissions November 2, 2006 2 Commissioner Dian M. Grueneich November 2, 2006 3 http://www.cpuc.ca.gov/PUBLISHED/REPORT/51604.htm Commissioner Dian M. Grueneich November 2, 2006 4 Energy Action Plan II Loading order continued "Pursue all cost-effective energy efficiency, first." Strong demand response and advanced metering

288

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01T23:59:59.000Z

289

On Demand Guarantees in Iran.  

E-Print Network [OSTI]

??On Demand Guarantees in Iran This thesis examines on demand guarantees in Iran concentrating on bid bonds and performance guarantees. The main guarantee types and… (more)

Ahvenainen, Laura

2009-01-01T23:59:59.000Z

290

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

We used experimental economics to test whether undergraduate students presented with a temperature forecast with uncertainty information in a table and bar graph format were able to use the extra information to interpret a given forecast. ...

Pricilla Marimo; Todd R. Kaplan; Ken Mylne; Martin Sharpe

291

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

FORECASTING THE ROLE OF RENEWABLES IN HAWAII Jayant SathayeFORECASTING THE ROLF OF RENEWABLES IN HAWAII J Sa and Henrythe Conservation Role of Renewables November 18, 1980 Page 2

Sathaye, Jayant

2013-01-01T23:59:59.000Z

292

Massachusetts state airport system plan forecasts.  

E-Print Network [OSTI]

This report is a first step toward updating the forecasts contained in the 1973 Massachusetts State System Plan. It begins with a presentation of the forecasting techniques currently available; it surveys and appraises the ...

Mathaisel, Dennis F. X.

293

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

For over 30 years, weather forecasting for the Antarctic continent and adjacent Southern Ocean has relied on weather satellites. Significant advancements in forecasting skill have come via the weather satellite. The advent of the high-resolution ...

Matthew A. Lazzara; Linda M. Keller; Charles R. Stearns; Jonathan E. Thom; George A. Weidner

2003-02-01T23:59:59.000Z

294

Forecasting Water Use in Texas Cities  

E-Print Network [OSTI]

In this research project, a methodology for automating the forecasting of municipal daily water use is developed and implemented in a microcomputer program called WATCAL. An automated forecast system is devised by modifying the previously...

Shaw, Douglas T.; Maidment, David R.

295

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect (OSTI)

In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

2009-03-01T23:59:59.000Z

296

Energy Demand Staff Scientist  

E-Print Network [OSTI]

Energy Demand in China Lynn Price Staff Scientist February 2, 2010 #12;Founded in 1988 Focused on End-Use Energy Efficiency ~ 40 Current Projects in China Collaborations with ~50 Institutions in China Researcher #12;Talk OutlineTalk Outline · Overview · China's energy use and CO2 emission trends · Energy

Eisen, Michael

297

Energy Demand Modeling  

Science Journals Connector (OSTI)

From the end of World War II until the early 1970s there was a strong and steady increase in the demand for energy. The abundant supplies of fossil and other ... an actual fall in the real price of energy of abou...

S. L. Schwartz

1980-01-01T23:59:59.000Z

298

Consensus Coal Production And Price Forecast For  

E-Print Network [OSTI]

Consensus Coal Production And Price Forecast For West Virginia: 2011 Update Prepared for the West December 2011 © Copyright 2011 WVU Research Corporation #12;#12;W.Va. Consensus Coal Forecast Update 2011 i Table of Contents Executive Summary 1 Recent Developments 3 Consensus Coal Production And Price Forecast

Mohaghegh, Shahab

299

Real time forecasting of near-future evolution  

Science Journals Connector (OSTI)

...evolving populations, each with a non-renewable supply of 96 beneficial mutations that...G. Callan, Jr, and M. Lassig 2008 Energy-dependent fitness: a quantitative model...Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA. pgerrish...

2012-01-01T23:59:59.000Z

300

Safeguards Education and Training: Short Term Supply vs. Demand  

SciTech Connect (OSTI)

Much has been written and discussed in the past several years about the effect of the aging nuclear workforce on the sustainability of the U.S. safeguards and security infrastructure. This paper discusses the 10-15 year supply and demand forecast for nuclear material control and accounting specialists. The demand side of the review includes control and accounting of the materials at U.S. DOE and NRC facilities, and the federal oversight of those MC&A programs. The cadre of experts referred to as 'MC&A Specialists' available to meet the demand goes beyond domestic MC&A to include international programs, regulatory and inspection support, and so on.

Mathews, Carrie E.; Crawford, Cary E.

2004-07-16T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation Analysis Papers > Annual Energy Outlook Forecast Evaluation Release Date: February 2005 Next Release Date: February 2006 Printer-friendly version Annual Energy Outlook Forecast Evaluation* Table 1.Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Printer Friendly Version Average Absolute Percent Error Variable AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Consumption Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8 Total Electricity Sales 1.9 2.0 2.3 2.3 2.3 2.4 Production Crude Oil Production 4.5 4.5 4.5 4.5 4.6 4.7

302

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

energy system. Observed refrigeration load and local ambient temperature from a Danish su- permarket renewable energy, is increasing, therefore a flexible energy system is needed. In the present ThesisLoad Forecasting of Supermarket Refrigeration Lisa Buth Rasmussen Kongens Lyngby 2013 M.Sc.-2013

303

Essays on macroeconomics and forecasting  

E-Print Network [OSTI]

explanatory variables. Compared to Stock and Watson (2002)�s models, the models proposed in this chapter can further allow me to select the factors structurally for each variable to be forecasted. I find advantages to using the structural dynamic factor...

Liu, Dandan

2006-10-30T23:59:59.000Z

304

Forecasting Solar Wind Speeds  

E-Print Network [OSTI]

By explicitly taking into account effects of Alfven waves, I derive from a simple energetics argument a fundamental relation which predicts solar wind (SW) speeds in the vicinity of the earth from physical properties on the sun. Kojima et al. recently found from their observations that a ratio of surface magnetic field strength to an expansion factor of open magnetic flux tubes is a good indicator of the SW speed. I show by using the derived relation that this nice correlation is an evidence of the Alfven wave which accelerates SW in expanding flux tubes. The observations further require that fluctuation amplitudes of magnetic field lines at the surface should be almost universal in different coronal holes, which needs to be tested by future observations.

Takeru K. Suzuki

2006-02-03T23:59:59.000Z

305

Residential Energy Demand Reduction Analysis and Monitoring Platform - REDRAMP  

Broader source: Energy.gov (indexed) [DOE]

Dramatic Peak Residential Dramatic Peak Residential Demand Reduction in the Desert Southwest Yahia Baghzouz Center for Energy Research University of Nevada, Las Vegas Golden, CO Overview * Project description * Subdivision energy efficiency features * Home energy monitoring * Demand side management * Feeder loading * Battery Energy Storage System * Future Work Team Members Project Objective and Methodology * The main objective is to reduce peak power demand of a housing subdivision by 65% (compared to housing development that is built to conventional code). * This objective will be achieved by - Energy efficient home construction with roof- integrated PV system - Demand Side Management - Battery Energy Storage System Project schematic Diagram Project Physical Location: Las Vegas, NV Red Rock Hotel/Casino

306

An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study  

SciTech Connect (OSTI)

This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

2011-01-17T23:59:59.000Z

307

Improved one day-ahead price forecasting using combined time series and artificial neural network models for the electricity market  

Science Journals Connector (OSTI)

The price forecasts embody crucial information for generators when planning bidding strategies to maximise profits. Therefore, generation companies need accurate price forecasting tools. Comparison of neural network and auto regressive integrated moving average (ARIMA) models to forecast commodity prices in previous researches showed that the artificial neural network (ANN) forecasts were considerably more accurate than traditional ARIMA models. This paper provides an accurate and efficient tool for short-term price forecasting based on the combination of ANN and ARIMA. Firstly, input variables for ANN are determined by time series analysis. This model relates the current prices to the values of past prices. Secondly, ANN is used for one day-ahead price forecasting. A three-layered feed-forward neural network algorithm is used for forecasting next-day electricity prices. The ANN model is then trained and tested using data from electricity market of Iran. According to previous studies, in the case of neural networks and ARIMA models, historical demand data do not significantly improve predictions. The results show that the combined ANNâ??ARIMA forecasts prices with high accuracy for short-term periods. Also, it is shown that policy-making strategies would be enhanced due to increased precision and reliability.

Ali Azadeh; Seyed Farid Ghaderi; Behnaz Pourvalikhan Nokhandan; Shima Nassiri

2011-01-01T23:59:59.000Z

308

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 51 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided into the energy- intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Module calculates

309

National Action Plan on Demand Response  

Broader source: Energy.gov (indexed) [DOE]

6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 ACTUAL FORECAST National Action Plan on Demand Response the feDeRal eneRgy RegulatoRy commission staff 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 12 6 3 9 National Action Plan on Demand Response THE FEDERAL ENERGY REGULATORY COMMISSION STAFF June 17, 2010 Docket No. AD09-10 Prepared with the support of The Brattle Group * GMMB * Customer Performance Group Definitive Insights * Eastern Research Group The opinions and views expressed in this staff report do not necessarily represent those of the Federal Energy Regulatory Commission, its Chairman, or individual Commissioners, and are not binding on the Commission.

310

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint  

SciTech Connect (OSTI)

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

2013-11-01T23:59:59.000Z

311

Annual Energy Outlook 2006 with Projections to 2030 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2006 with Projections to 2030 Only GII produces a comprehensive energy projection with a time horizon similar to that of AEO2006. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2006 projections. Economic Growth In the AEO2006 reference case, the projected growth in real GDP, based on 2000 chain-weighted dollars, is 3.0 percent per year from 2004 to 2030 (Table 19). For the period from 2004 to 2025, real GDP growth in the AEO2006 reference case is similar to the average annual growth projected in AEO2005. The AEO2006 projections of economic growth are based on the August short-term forecast of GII, extended by EIA through 2030 and modified to reflect EIAÂ’s view on energy prices, demand, and production.

312

Integrated offering strategy for profit enhancement of distributed resources and demand response in microgrids considering system uncertainties  

Science Journals Connector (OSTI)

Abstract Due to the uncertain nature and limited predictability of wind and PV generated power, these resources participating in most of electricity markets are subject to significant deviation penalties during market settlements. In order to balance the unpredicted wind and PV power variations, system operators need to schedule additional reserves. This paper presents the optimal integrated participation model of wind and PV energy including demand response, storage devices, and dispatchable distributed generations in microgrids or virtual microgrids to increase their revenues in the intra-market. This market is considered 3–7 h before the delivered time, so that the amount of the contracted energy could be updated to reduce the produced power deviation of microgrid. A stochastic programming approach is considered in the development of the proposed bidding strategies for microgrid producers and loads. The optimization model is characterized by making the analysis of several scenarios and simultaneously treating three kinds of uncertainty including wind and PV power, intra-market, and imbalance prices. In order to predict these uncertainty variables, a neuro-fuzzy based approach has been applied. Historic data are used to forecast future prices and wind and PV power production in the adjustment markets. Also, a probabilistic approach based on the error of forecasted and real historic data is considered for estimating the future IM and imbalance prices of wind and PV produced power. Further, a test case is applied to example the microgrid using the Spanish market rules during one week, month, and year period to illustrate the potential benefits of the proposed joint biding strategy. The simulations results, carried out by MATLAB/optimization toolbox.

H. Shayeghi; B. Sobhani

2014-01-01T23:59:59.000Z

313

China's Coal: Demand, Constraints, and Externalities  

SciTech Connect (OSTI)

This study analyzes China's coal industry by focusing on four related areas. First, data are reviewed to identify the major drivers of historical and future coal demand. Second, resource constraints and transport bottlenecks are analyzed to evaluate demand and growth scenarios. The third area assesses the physical requirements of substituting coal demand growth with other primary energy forms. Finally, the study examines the carbon- and environmental implications of China's past and future coal consumption. There are three sections that address these areas by identifying particular characteristics of China's coal industry, quantifying factors driving demand, and analyzing supply scenarios: (1) reviews the range of Chinese and international estimates of remaining coal reserves and resources as well as key characteristics of China's coal industry including historical production, resource requirements, and prices; (2) quantifies the largest drivers of coal usage to produce a bottom-up reference projection of 2025 coal demand; and (3) analyzes coal supply constraints, substitution options, and environmental externalities. Finally, the last section presents conclusions on the role of coal in China's ongoing energy and economic development. China has been, is, and will continue to be a coal-powered economy. In 2007 Chinese coal production contained more energy than total Middle Eastern oil production. The rapid growth of coal demand after 2001 created supply strains and bottlenecks that raise questions about sustainability. Urbanization, heavy industrial growth, and increasing per-capita income are the primary interrelated drivers of rising coal usage. In 2007, the power sector, iron and steel, and cement production accounted for 66% of coal consumption. Power generation is becoming more efficient, but even extensive roll-out of the highest efficiency units would save only 14% of projected 2025 coal demand for the power sector. A new wedge of future coal consumption is likely to come from the burgeoning coal-liquefaction and chemicals industries. If coal to chemicals capacity reaches 70 million tonnes and coal-to-liquids capacity reaches 60 million tonnes, coal feedstock requirements would add an additional 450 million tonnes by 2025. Even with more efficient growth among these drivers, China's annual coal demand is expected to reach 3.9 to 4.3 billion tonnes by 2025. Central government support for nuclear and renewable energy has not reversed China's growing dependence on coal for primary energy. Substitution is a matter of scale: offsetting one year of recent coal demand growth of 200 million tonnes would require 107 billion cubic meters of natural gas (compared to 2007 growth of 13 BCM), 48 GW of nuclear (compared to 2007 growth of 2 GW), or 86 GW of hydropower capacity (compared to 2007 growth of 16 GW). Ongoing dependence on coal reduces China's ability to mitigate carbon dioxide emissions growth. If coal demand remains on a high growth path, carbon dioxide emissions from coal combustion alone would exceed total US energy-related carbon emissions by 2010. Within China's coal-dominated energy system, domestic transportation has emerged as the largest bottleneck for coal industry growth and is likely to remain a constraint to further expansion. China has a low proportion of high-quality reserves, but is producing its best coal first. Declining quality will further strain production and transport capacity. Furthermore, transporting coal to users has overloaded the train system and dramatically increased truck use, raising transportation oil demand. Growing international imports have helped to offset domestic transport bottlenecks. In the long term, import demand is likely to exceed 200 million tonnes by 2025, significantly impacting regional markets.

Aden, Nathaniel; Fridley, David; Zheng, Nina

2009-07-01T23:59:59.000Z

314

Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services  

SciTech Connect (OSTI)

The Pacific Gas and Electric Company (PG&E) is conducting a pilot program to investigate the technical feasibility of bidding certain demand response (DR) resources into the California Independent System Operator's (CAISO) day-ahead market for ancillary services nonspinning reserve. Three facilities, a retail store, a local government office building, and a bakery, are recruited into the pilot program. For each facility, hourly demand, and load curtailment potential are forecasted two days ahead and submitted to the CAISO the day before the operation as an available resource. These DR resources are optimized against all other generation resources in the CAISO ancillary service. Each facility is equipped with four-second real time telemetry equipment to ensure resource accountability and visibility to CAISO operators. When CAISO requests DR resources, PG&E's OpenADR (Open Automated DR) communications infrastructure is utilized to deliver DR signals to the facilities energy management and control systems (EMCS). The pre-programmed DR strategies are triggered without a human in the loop. This paper describes the automated system architecture and the flow of information to trigger and monitor the performance of the DR events. We outline the DR strategies at each of the participating facilities. At one site a real time electric measurement feedback loop is implemented to assure the delivery of CAISO dispatched demand reductions. Finally, we present results from each of the facilities and discuss findings.

Kiliccote, Sila; Piette, Mary Ann; Ghatikar, Girish; Koch, Ed; Hennage, Dan; Hernandez, John; Chiu, Albert; Sezgen, Osman; Goodin, John

2009-11-06T23:59:59.000Z

315

Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

and clothes drying. In addition to the major equipment-driven and clothes drying. In addition to the major equipment-driven end-uses, the average energy consumption per household is projected for other electric and nonelectric Energy Information Administration/Assumptions to the Annual Energy Outlook 2006 19 Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central South Atlantic Mountain Figure 5. United States Census Divisions Source:Energy Information Administration,Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2006) Release date: March 2006

316

Ocean indicators Current knowledge and future directions  

E-Print Network [OSTI]

Ocean indicators Current knowledge and future directions Brian Burke, NOAA Fisheries Brian.Burke@noaa.gov #12;· Review of ocean indicator work · Forecasting · Indicator gaps and the importance of mechanistic understanding · Plugging in to management #12;Haeseker et al. 2012 Ocean survival is low and variable #12;-10 -5

317

CALIFORNIA ENERGY CALIFORNIA ENERGY DEMAND 2010-2020  

E-Print Network [OSTI]

prepared the industrial forecast. Mark Ciminelli forecasted energy for transportation, communication developed the energy efficiency program estimates. Glen Sharp prepared the residential sector forecast ................................................................................................................... 2 EndUser Natural Gas Forecast Results

318

Forecasting wind speed financial return  

E-Print Network [OSTI]

The prediction of wind speed is very important when dealing with the production of energy through wind turbines. In this paper, we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model that has been shown to be able to reproduce accurately the statistical behavior of wind speed. The model is used to forecast, one step ahead, wind speed. In order to check the validity of the model we show, as indicator of goodness, the root mean square error and mean absolute error between real data and predicted ones. We also compare our forecasting results with those of a persistence model. At last, we show an application of the model to predict financial indicators like the Internal Rate of Return, Duration and Convexity.

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

319

A proposed methodology for medium-range maximum demand anticipation and application  

Science Journals Connector (OSTI)

One to three years' anticipation of monthly and weekly peak demand is required to prepare maintenance schedules, develop power pooling agreements, select peaking capacity and provide data required by certain reliability coordinating centers. A total monthly forecast of the maximum demand is deduced and computed for the three years up to April 1981. This is accomplished for an important electrical network in Egypt. The anticipated maximum demand is executed for El-Mehalla El-Kubra city network. This network has an industrial and residential daily load characteristic. Direct monthly maximum demand forecasting is executed by separate treatment of weather-independent and weather-induced demand. The required forecast is derived by two methodologies: the probabilistic extrapolation-correlation, and that suggested by the authors. Daily and monthly data have been collected for more reliable determination of weather load models. Complete analysis, discussion and comments on the results are presented, and the results compared. This comparison reveals that an acceptable and reasonable percentage error is obtained on applying the proposed methodology.

M.S. Kandil; M.Helmy El-Maghraby; H. El-Dosouky

1981-01-01T23:59:59.000Z

320

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

Av:l.at:i.on Fuel Total Oil Demand 0:!.1 Demand w:t thoutthe ef feet of oil prices on energy demand and supply, \\veSince electric:! ty prices oil prices, the demand for will :

Sathaye, Jayant

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Demand Response | Department of Energy  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Demand Response Demand Response Demand Response Demand Response Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. Demand response programs are being used by electric system planners and operators as resource options for balancing supply and demand. Such programs can lower the cost of electricity in wholesale markets, and in turn, lead to lower retail rates. Methods of engaging customers in demand response efforts include offering time-based rates such as time-of-use pricing, critical peak pricing, variable peak pricing, real time pricing, and critical peak rebates. It also includes direct load control programs which provide the

322

Understanding and Analysing Energy Demand  

Science Journals Connector (OSTI)

This chapter introduces the concept of energy demand using basic micro-economics and presents the three-stage decision making process of energy demand. It then provides a set of simple ... (such as price and inco...

Subhes C. Bhattacharyya

2011-01-01T23:59:59.000Z

323

Demand Response: Load Management Programs  

E-Print Network [OSTI]

CenterPoint Load Management Programs CATEE Conference October, 2012 Agenda Outline I. General Demand Response Definition II. General Demand Response Program Rules III. CenterPoint Commercial Program IV. CenterPoint Residential Programs... V. Residential Discussion Points Demand Response Definition of load management per energy efficiency rule 25.181: ? Load control activities that result in a reduction in peak demand, or a shifting of energy usage from a peak to an off...

Simon, J.

2012-01-01T23:59:59.000Z

324

Marketing Demand-Side Management  

E-Print Network [OSTI]

they the only game in town, enjoying a captive market. Demand-side management (DSM) again surfaced as a method for increasing customer value and meeting these competitive challenges. In designing and implementing demand-side management (DSM) programs we... have learned a great deal about what it takes to market and sell DSM. This paper focuses on how to successfully market demand-side management. KEY STEPS TO MARKETING DEMAND-SIDE MANAGEMENT Management Commitment The first key element in marketing...

O'Neill, M. L.

1988-01-01T23:59:59.000Z

325

Demand Charges | Open Energy Information  

Open Energy Info (EERE)

Charges Jump to: navigation, search Retrieved from "http:en.openei.orgwindex.php?titleDemandCharges&oldid488967"...

326

(2013) 128 Data Center Demand Response: Avoiding the Coincident Peak via  

E-Print Network [OSTI]

(2013) 1­28 Data Center Demand Response: Avoiding the Coincident Peak via Workload Shifting.chen@hp.com Abstract Demand response is a crucial aspect of the future smart grid. It has the potential to provide centers' participation in demand response is becoming increasingly important given their high

Wierman, Adam

327

U.S. Energy Demand: Some Low Energy Futures  

Science Journals Connector (OSTI)

...sophistication for energy consumption. | Journal Article...ac-tivities related to fuel conservation. The...processes, not only in fuel con-servation...History ofthe Steam Engine (Cambridge Univ...coal-fired steam to diesel) but much is at-tributable...sophistication for energy consumption. The scenarios...

1978-04-14T23:59:59.000Z

328

U.S. Energy Demand: Some Low Energy Futures  

Science Journals Connector (OSTI)

...energy consumption per unit of output fell...I to 1.5 percent per year from 1950 to...en-ergy consumption per capita rose by 50...Between 1946 and 1973 amenities such as...enable resource production from low-grade ores...Exporting Countries (OPEC) (fall 1973) and...

1978-04-14T23:59:59.000Z

329

Assessment of Demand Response Resource  

E-Print Network [OSTI]

Assessment of Demand Response Resource Potentials for PGE and Pacific Power Prepared for: Portland January 15, 2004 K:\\Projects\\2003-53 (PGE,PC) Assess Demand Response\\Report\\Revised Report_011504.doc #12;#12;quantec Assessment of Demand Response Resource Potentials for I-1 PGE and Pacific Power I. Introduction

330

ERCOT Demand Response Paul Wattles  

E-Print Network [OSTI]

ERCOT Demand Response Paul Wattles Senior Analyst, Market Design & Development, ERCOT Whitacre;Definitions of Demand Response · `The short-term adjustment of energy use by consumers in response to price to market or reliability conditions.' (NAESB) #12;Definitions of Demand Response · The common threads

Mohsenian-Rad, Hamed

331

Pricing data center demand response  

Science Journals Connector (OSTI)

Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, ... Keywords: data center, demand response, power network, prediction based pricing

Zhenhua Liu; Iris Liu; Steven Low; Adam Wierman

2014-06-01T23:59:59.000Z

332

Choosing an electrical energy future for the Pacific Northwest: an Alternative Scenario  

SciTech Connect (OSTI)

An Alternative Scenario for the electric energy future of the Pacific Northwest is presented. The Scenario includes an analysis of each major end use of electricity in the residential, commercial, manufacturing, and agricultural sectors. This approach affords the most direct means of projecting the likely long-term growth in consumption and the opportunities for increasing the efficiency with which electricity is used in each instance. The total demand for electricity by these end uses then provides a basis for determining whether additional central station generation is required to 1995. A projection of total demand for electricity depends on the combination of many independent variables and assumptions. Thus, the approach is a resilient one; no single assumption or set of linked assumptions dominates the analysis. End-use analysis allows policymakers to visualize the benefits of alternative programs, and to make comparison with the findings of other studies. It differs from the traditional load forecasts for the Pacific Northwest, which until recently were based largely on straightforward extrapolations of historical trends in the growth of electrical demand. The Scenario addresses the supply potential of alternative energy sources. Data are compiled for 1975, 1985, and 1995 in each end-use sector.

Cavanagh, R.C.; Mott, L.; Beers, J.R.; Lash, T.L.

1980-08-01T23:59:59.000Z

333

Agent-based coordination techniques for matching supply and demand in energy networks  

Science Journals Connector (OSTI)

There is a lot of effort directed toward realizing the power network of the future. The future power network is expected to depend on a large number of renewable energy resources connected directly to the low and medium voltage power network. Demand ... Keywords: Supply and demand matching, market and non-market algorithms, multi-agent systems

Rashad Badawy; Benjamin Hirsch; Sahin Albayrak

2010-12-01T23:59:59.000Z

334

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

Poulin, L.

2013-01-01T23:59:59.000Z

335

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts  

Science Journals Connector (OSTI)

Bayesian model averaging (BMA) probability quantitative precipitation forecast (PQPF) models were established by calibrating their parameters using 1–7-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 ...

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

336

Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts  

Science Journals Connector (OSTI)

The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (...

Felix Fundel; Andre Walser; Mark A. Liniger; Christoph Frei; Christof Appenzeller

2010-01-01T23:59:59.000Z

337

Overview of Demand Response  

Broader source: Energy.gov (indexed) [DOE]

08 PJM 08 PJM www.pjm.com ©2003 PJM Overview of Demand Response PJM ©2008 PJM www.pjm.com ©2003 PJM Growth, Statistics, and Current Footprint AEP, Dayton, ComEd, & DUQ Dominion Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Generating Units 1,200 + Generation Capacity 165,000 MW Peak Load 144,644 MW Transmission Miles 56,070 Area (Square Miles) 164,250 Members 500 + Population Served 51 Million Area Served 13 States and DC Current PJM RTO Statistics Current PJM RTO Statistics PJM Mid-Atlantic Integrations completed as of May 1 st , 2005 ©2008 PJM

338

Possible global warming futures Minh Ha-Duong  

E-Print Network [OSTI]

Possible global warming futures Minh Ha-Duong Minh.Ha.Duong@cmu.edu CNRS, France HDGC, Carnegie Mellon Possible global warming futures ­ p.1/36 #12;SRES: Forecasts or scenarios? +5.5 C in 2100 the controversy using imprecise probabilities, a more general information theory. . . Possible global warming

339

What Do Consumers Believe About Future Gasoline Soren T. Anderson  

E-Print Network [OSTI]

What Do Consumers Believe About Future Gasoline Prices? Soren T. Anderson Michigan State University of consumers about their expectations of future gasoline prices. Overall, we find that consumer beliefs follow a random walk, which we deem a reasonable forecast of gasoline prices, but we find a deviation from

Silver, Whendee

340

U.S. diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon  

U.S. Energy Information Administration (EIA) Indexed Site

diesel fuel price forecast to be 1 penny lower this summer diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon The retail price of diesel fuel is expected to average $3.94 a gallon during the summer driving season that which runs from April through September. That's close to last summer's pump price of $3.95, according to the latest monthly energy outlook from the U.S. Energy Information Administration. Demand for distillate fuel, which includes diesel fuel, is expected to be up less than 1 percent from last summer. Daily production of distillate fuel at U.S. refineries is forecast to be 70,000 barrels higher this summer. With domestic distillate output exceeding demand, U.S. net exports of distillate fuel are expected to average 830,000 barrels per day this summer. That's down 12 percent from last summer's

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability?A Review  

Science Journals Connector (OSTI)

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

R. M. Holmukhe; Mrs. Sunita Dhumale; Mr. P. S. Chaudhari; Mr. P. P. Kulkarni

2010-01-01T23:59:59.000Z

342

The state-of-the-art in air transportation demand and systems analysis : a report on the proceedings of a workshop sponsored by the Civil Aeronautics Board, Department of Transportation, and National Aeronautics and Space Administration (June 1975)  

E-Print Network [OSTI]

Introduction and summary: Forecasting air transportation demand has indeed become a complex and risky business in recent years, especially in view of unpredictable fuel prices, high inflation rates, a declining rate of ...

Taneja, Nawal K.

1975-01-01T23:59:59.000Z

343

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

H Tables H Tables Appendix H Comparisons With Other Forecasts, and Performance of Past IEO Forecasts for 1990, 1995, and 2000 Forecast Comparisons Three organizations provide forecasts comparable with those in the International Energy Outlook 2005 (IEO2005). The International Energy Agency (IEA) provides “business as usual” projections to the year 2030 in its World Energy Outlook 2004; Petroleum Economics, Ltd. (PEL) publishes world energy forecasts to 2025; and Petroleum Industry Research Associates (PIRA) provides projections to 2015. For this comparison, 2002 is used as the base year for all the forecasts, and the comparisons extend to 2025. Although IEA’s forecast extends to 2030, it does not publish a projection for 2025. In addition to forecasts from other organizations, the IEO2005 projections are also compared with those in last year’s report (IEO2004). Because 2002 data were not available when IEO2004 forecasts were prepared, the growth rates from IEO2004 are computed from 2001.

344

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

345

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

346

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

Huge market forecast for linear LDPE ... It now appears that the success of the new technology, which rests largely on energy and equipment cost savings, could be overwhelming. ...

1980-08-25T23:59:59.000Z

347

A B S T R A C T Forecasting in a risky situation is a very important  

E-Print Network [OSTI]

. In this research the target item for prediction is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and its very sensitive on oil prices and the demand and supply ratio. The main idea of Artificial Intelligence, Financial Forecasting such as Stock Price Predictions entered in new phase. Sadly

Paris-Sud XI, Université de

348

The importance of food demand management for climate mitigation  

E-Print Network [OSTI]

and fertiliser, and the inclusion of climate change as a driver of yield changes and irrigation demand. This would enable estimation of how shortfalls in irrigation water availability might affect future food production. Bioenergy scenarios also lie outside... the scope of the current paper; unless food demand patterns change significantly, there seems to be little spare land for bioenergy developments without a reduction of food availability. However, it is important to note that the model results we present...

Bajželj, Bojana; Richards, Keith S.; Allwood, Julian M.; Smith, Pete; Dennis, John S.; Curmi, Elizabeth; Gilligan, Christopher A.

2014-08-31T23:59:59.000Z

349

Price Movements Related to Supply/Demand Balance  

Gasoline and Diesel Fuel Update (EIA)

4 4 Notes: EIA sees a tenuous supply/demand balance over the remainder of 2001 and into the beginning of 2002, as illustrated by the low OECD inventory levels. Global inventories remain low, and need to recover to more adequate levels in order to avoid continued price volatility. While we saw some stocking in April and May, typical third quarter stock builds may not occur. Even with Iraqi oil exports resuming in early July, OPEC was going to need to increase its oil production to account for demand increases over the 2nd half of the year to prevent stocks from falling further. However, they not only haven't agreed to increase production, but agreed to cut production quotas by 1 million barrels per day beginning on September 1! EIA's forecast of a continued low stock cushion implies we not only

350

Assumptions to the Annual Energy Outlook 2001 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Comleted Copy in PDF Format Comleted Copy in PDF Format Related Links Annual Energy Outlook 2001 Supplemental Data to the AEO 2001 NEMS Conference To Forecasting Home Page EIA Homepage Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 19). The

351

Assumptions to the Annual Energy Outlook 2000 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. The energy-intensive industries are modeled through the use of a detailed process flow accounting procedure, whereas the nonenergy-intensive and the nonmanufacturing industries are modeled with substantially less detail (Table 14). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated by using the SEDS24 data.

352

Assumptions to the Annual Energy Outlook 1999 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

commercial.gif (5196 bytes) commercial.gif (5196 bytes) The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. The definition of the commercial sector is consistent with EIAÂ’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings, however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

353

Annual Energy Outlook Forecast Evaluation - Table 1. Forecast Evaluations:  

Gasoline and Diesel Fuel Update (EIA)

Average Absolute Percent Errors from AEO Forecast Evaluations: Average Absolute Percent Errors from AEO Forecast Evaluations: 1996 to 2000 Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Variable 1996 Evaluation: AEO82 to AEO93 1997 Evaluation: AEO82 to AEO97 1998 Evaluation: AEO82 to AEO98 1999 Evaluation: AEO82 to AEO99 2000 Evaluation: AEO82 to AEO2000 Consumption Total Energy Consumption 1.8 1.6 1.7 1.7 1.8 Total Petroleum Consumption 3.2 2.8 2.9 2.8 2.9 Total Natural Gas Consumption 6.0 5.8 5.7 5.6 5.6 Total Coal Consumption 2.9 2.7 3.0 3.2 3.3 Total Electricity Sales 1.8 1.6 1.7 1.8 2.0 Production Crude Oil Production 5.1 4.2 4.3 4.5 4.5

354

Optimal combined wind power forecasts using exogeneous variables  

E-Print Network [OSTI]

Optimal combined wind power forecasts using exogeneous variables Fannar ¨Orn Thordarson Kongens of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

355

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened at a ...

Jing-Shan Hong; Chin-Tzu Fong; Ling-Feng Hsiao; Yi-Chiang Yu; Chian-You Tzeng

356

EIA - Assumptions to the Annual Energy Outlook 2010 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2010 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock,

357

Future Accelerators (?)  

E-Print Network [OSTI]

I describe the future accelerator facilities that are currently foreseen for electroweak scale physics, neutrino physics, and nuclear structure. I will explore the physics justification for these machines, and suggest how the case for future accelerators can be made.

John Womersley

2003-08-09T23:59:59.000Z

358

EIA-Assumptions to the Annual Energy Outlook - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2007 Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

359

Demand Response Opportunities and Enabling Technologies for Data Centers:  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Demand Response Opportunities and Enabling Technologies for Data Centers: Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies Title Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies Publication Type Report LBNL Report Number LBNL-5763E Year of Publication 2012 Authors Ghatikar, Girish, Venkata Ganti, Nance Matson, and Mary Ann Piette Publisher PG&E/SDG&E/CEC/LBNL Keywords communication and standards, control systems, data centers, demand response, enabling technologies, end-use technologies, load migration, market sectors, technologies Abstract The energy use in data centers is increasing and, in particular, impacting the data center energy cost and electric grid reliability during peak and high price periods. As per the 2007 U.S. Environmental Protection Agency (EPA), in the Pacific Gas and Electric Company territory, data centers are estimated to consume 500 megawatts of annual peak electricity. The 2011 data confirm the increase in data center energy use, although it is slightly lower than the EPA forecast. Previous studies have suggested that data centers have significant potential to integrate with supply-side programs to reduce peak loads. In collaboration with California data centers, utilities, and technology vendors, this study conducted field tests to improve the understanding of the demand response opportunities in data centers. The study evaluated an initial set of control and load migration strategies and economic feasibility for four data centers. The findings show that with minimal or no impact to data center operations a demand savings of 25% at the data center level or 10% to 12% at the whole building level can be achieved with strategies for cooling and IT equipment, and load migration. These findings should accelerate the grid-responsiveness of data centers through technology development, integration with the demand response programs, and provide operational cost savings.

360

Hawaiian Electric Company Demand Response Roadmap Project  

E-Print Network [OSTI]

of control. Water heater demand response options are notcurrent water heater and air conditioning demand responsecustomer response Demand response water heater participation

Levy, Roger

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Coordination of Energy Efficiency and Demand Response  

E-Print Network [OSTI]

and D. Kathan (2009). Demand Response in U.S. ElectricityEnergy Financial Group. Demand Response Research Center [2008). Assessment of Demand Response and Advanced Metering.

Goldman, Charles

2010-01-01T23:59:59.000Z

362

Hawaiian Electric Company Demand Response Roadmap Project  

E-Print Network [OSTI]

Like HECO actual utility demand response implementations canindustry-wide utility demand response applications tend toobjective. Figure 4. Demand Response Objectives 17  

Levy, Roger

2014-01-01T23:59:59.000Z

363

Installation and Commissioning Automated Demand Response Systems  

E-Print Network [OSTI]

their partnership in demand response automation research andand Techniques for Demand Response. LBNL Report 59975. Mayof Fully Automated Demand Response in Large Facilities.

Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

2008-01-01T23:59:59.000Z

364

Barrier Immune Radio Communications for Demand Response  

E-Print Network [OSTI]

of Fully Automated Demand Response in Large Facilities,”Fully Automated Demand Response Tests in Large Facilities.for Automated Demand Response. Technical Document to

Rubinstein, Francis

2010-01-01T23:59:59.000Z

365

Retail Demand Response in Southwest Power Pool  

E-Print Network [OSTI]

23 ii Retail Demand Response in SPP List of Figures and10 Figure 3. Demand Response Resources by11 Figure 4. Existing Demand Response Resources by Type of

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

366

Home Network Technologies and Automating Demand Response  

E-Print Network [OSTI]

and Automating Demand Response Charles McParland, Lawrenceand Automating Demand Response Charles McParland, LBNLCommercial and Residential Demand Response Overview of the

McParland, Charles

2010-01-01T23:59:59.000Z

367

Wireless Demand Response Controls for HVAC Systems  

E-Print Network [OSTI]

Strategies Linking Demand Response and Energy Efficiency,”Fully Automated Demand Response Tests in Large Facilities,technical support from the Demand Response Research Center (

Federspiel, Clifford

2010-01-01T23:59:59.000Z

368

Strategies for Demand Response in Commercial Buildings  

E-Print Network [OSTI]

Fully Automated Demand Response Tests in Large Facilities”of Fully Automated Demand Response in Large Facilities”,was coordinated by the Demand Response Research Center and

Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

2006-01-01T23:59:59.000Z

369

Option Value of Electricity Demand Response  

E-Print Network [OSTI]

Table 1. “Economic” demand response and real time pricing (Implications of Demand Response Programs in CompetitiveAdvanced Metering, and Demand Response in Electricity

Sezgen, Osman; Goldman, Charles; Krishnarao, P.

2005-01-01T23:59:59.000Z

370

Coordination of Energy Efficiency and Demand Response  

E-Print Network [OSTI]

of Energy demand-side management energy information systemdemand response. Demand-side management (DSM) program goalsa goal for demand-side management (DSM) coordination and

Goldman, Charles

2010-01-01T23:59:59.000Z

371

China's Coal: Demand, Constraints, and Externalities  

E-Print Network [OSTI]

raising transportation oil demand. Growing internationalcoal by wire could reduce oil demand by stemming coal roadEastern oil production. The rapid growth of coal demand

Aden, Nathaniel

2010-01-01T23:59:59.000Z

372

Coupling Renewable Energy Supply with Deferrable Demand  

E-Print Network [OSTI]

World: Renewable Energy and Demand Response Proliferation intogether the renewable energy and demand response communityimpacts of renewable energy and demand response integration

Papavasiliou, Anthony

2011-01-01T23:59:59.000Z

373

Coordination of Energy Efficiency and Demand Response  

E-Print Network [OSTI]

District Small Business Summer Solutions: Energy and DemandSummer Solutions: Energy and Demand Impacts Monthly Energy> B-2 Coordination of Energy Efficiency and Demand Response

Goldman, Charles

2010-01-01T23:59:59.000Z

374

electricity demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description The New Zealand Ministry of Economic Development publishes energy data including many datasets related to electricity. Included here are three electricity consumption and demand datasets, specifically: annual observed electricity consumption by sector (1974 to 2009); observed percentage of consumers by sector (2002 - 2009); and regional electricity demand, as a percentage of total demand (2009). Source New Zealand Ministry of Economic Development Date Released Unknown Date Updated July 03rd, 2009 (5 years ago) Keywords Electricity Consumption electricity demand energy use by sector New Zealand Data application/vnd.ms-excel icon Electricity Consumption by Sector (1974 - 2009) (xls, 46.1 KiB) application/vnd.ms-excel icon Percentage of Consumers by Sector (2002 - 2009) (xls, 43.5 KiB)

375

Annual World Oil Demand Growth  

Gasoline and Diesel Fuel Update (EIA)

6 6 Notes: Following relatively small increases of 1.3 million barrels per day in 1999 and 0.9 million barrels per day in 2000, EIA is estimating world demand may grow by 1.6 million barrels per day in 2001. Of this increase, about 3/5 comes from non-OECD countries, while U.S. oil demand growth represents more than half of the growth projected in OECD countries. Demand in Asia grew steadily during most of the 1990s, with 1991-1997 average growth per year at just above 0.8 million barrels per day. However, in 1998, demand dropped by 0.3 million barrels per day as a result of the Asian economic crisis that year. Since 1998, annual growth in oil demand has rebounded, but has not yet reached the average growth seen during 1991-1997. In the Former Soviet Union, oil demand plummeted during most of the

376

Forecast of geothermal drilling activity  

SciTech Connect (OSTI)

The numbers of each type of geothermal well expected to be drilled in the United States for each 5-year period to 2000 AD are specified. Forecasts of the growth of geothermally supplied electric power and direct heat uses are presented. The different types of geothermal wells needed to support the forecasted capacity are quantified, including differentiation of the number of wells to be drilled at each major geothermal resource for electric power production. The rate of growth of electric capacity at geothermal resource areas is expected to be 15 to 25% per year (after an initial critical size is reached) until natural or economic limits are approached. Five resource areas in the United States should grow to significant capacity by the end of the century (The Geysers; Imperial Valley; Valles Caldera, NM; Roosevelt Hot Springs, UT; and northern Nevada). About 3800 geothermal wells are expected to be drilled in support of all electric power projects in the United States between 1981 and 2000 AD. Half of the wells are expected to be drilled in the Imperial Valley. The Geysers area is expected to retain most of the drilling activity for the next 5 years. By the 1990's, the Imperial Valley is expected to contain most of the drilling activity.

Brown, G.L.; Mansure, A.J.

1981-10-01T23:59:59.000Z

377

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

378

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near-surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

379

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near­surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

380

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

deficits. After solid 3% growth this year, real GDP growth will recede a bit for the next two years. New household spending. Real GDP will firm above 3% in 2015. · The pace of growth in China has continuedUHERO FORECAST PROJECT DECEMBER 5, 2014 Asia-Pacific Forecast: Press Version: Embargoed Until 2

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surfaceAmending Numerical Weather Prediction forecasts using GPS Integrated Water Vapour: a case study to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents

Stoffelen, Ad

382

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates...

Ana Corberán-Vallet; José D. Bermúdez; José V. Segura…

2010-01-01T23:59:59.000Z

383

ANL Software Improves Wind Power Forecasting | Department of...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

principal investigator for the project. For wind power point forecasting, ARGUS PRIMA trains a neural network using data from weather forecasts, observations, and actual wind...

384

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network [OSTI]

??The research deals with improved prediction of runway usage for noise forecast. Since the accuracy of the noise forecast depends on the robustness of runway… (more)

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

385

The Wind Forecast Improvement Project (WFIP): A Public/Private...  

Energy Savers [EERE]

Improvement Project (WFIP): A PublicPrivate Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The Wind Forecast...

386

PBL FY 2002 Third Quarter Review Forecast of Generation Accumulated...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Power Business Line Generation Accumulated Net Revenues Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) FY 2002 Third Quarter Review Forecast in Millions...

387

An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units  

Science Journals Connector (OSTI)

Abstract The increasing awareness on the risky state of conventional energy sources in terms of future energy supply security and health of environment has promoted the research activities on alternative energy systems. However, due to the fact that the power production of main alternative sources such as wind and solar is directly related with meteorological conditions, these sources should be combined with dispatchable energy sources in a hybrid combination in order to ensure security of demand supply. In this study, the evaluation of such a hybrid system consisting of wind, solar, hydrogen and thermal power systems in the concept of virtual power plant strategy is realized. An economic operation-based load dispatching strategy that can interactively adapt to the real measured wind and solar power production values is proposed. The adaptation of the load dispatching algorithm is provided by the update mechanism employed in the meteorological condition forecasting algorithms provided by the combination of Empirical Mode Decomposition, Cascade-Forward Neural Network and Linear Model through a fusion strategy. Thus, the effects of the stochastic nature of solar and wind energy systems are better overcome in order to participate in the electricity market with higher benefits.

A. Tascikaraoglu; O. Erdinc; M. Uzunoglu; A. Karakas

2014-01-01T23:59:59.000Z

388

Harnessing the power of demand  

SciTech Connect (OSTI)

Demand response can provide a series of economic services to the market and also provide ''insurance value'' under low-likelihood, but high-impact circumstances in which grid reliablity is enhanced. Here is how ISOs and RTOs are fostering demand response within wholesale electricity markets. (author)

Sheffrin, Anjali; Yoshimura, Henry; LaPlante, David; Neenan, Bernard

2008-03-15T23:59:59.000Z

389

China, India demand cushions prices  

SciTech Connect (OSTI)

Despite the hopes of coal consumers, coal prices did not plummet in 2006 as demand stayed firm. China and India's growing economies, coupled with solid supply-demand fundamentals in North America and Europe, and highly volatile prices for alternatives are likely to keep physical coal prices from wide swings in the coming year.

Boyle, M.

2006-11-15T23:59:59.000Z

390

Honeywell Demonstrates Automated Demand Response Benefits for...  

Broader source: Energy.gov (indexed) [DOE]

Honeywell Demonstrates Automated Demand Response Benefits for Utility, Commercial, and Industrial Customers Honeywell Demonstrates Automated Demand Response Benefits for Utility,...

391

Retail Demand Response in Southwest Power Pool  

E-Print Network [OSTI]

Data Collection for Demand-side Management for QualifyingPrepared by Demand-side Management Task Force of the

Bharvirkar, Ranjit

2009-01-01T23:59:59.000Z

392

Barrier Immune Radio Communications for Demand Response  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Barrier Immune Radio Communications for Demand Response Barrier Immune Radio Communications for Demand Response Title Barrier Immune Radio Communications for Demand Response Publication Type Report LBNL Report Number LBNL-2294e Year of Publication 2009 Authors Rubinstein, Francis M., Girish Ghatikar, Jessica Granderson, Paul Haugen, Carlos Romero, and David S. Watson Keywords technologies Abstract Various wireless technologies were field-tested in a six-story laboratory building to identify wireless technologies that can scale for future DR applications through very low node density power consumption, and unit cost. Data analysis included analysis of the signal-to-noise ratio (SNR), packet loss, and link quality at varying power levels and node densities. The narrowband technologies performed well, penetrating the floors of the building with little loss and exhibiting better range than the wideband technology. 900 MHz provided full coverage at 1 watt and substantially complete coverage at 500 mW at the test site. 900 MHz was able to provide full coverage at 100 mW with only one additional relay transmitter, and was the highest-performing technology in the study. 2.4 GHz could not provide full coverage with only a single transmitter at the highest power level tested (63 mW). However, substantially complete coverage was provided at 2.4 GHz at 63 mW with the addition of one repeater node.

393

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting  

Science Journals Connector (OSTI)

Abstract Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily.

Zhongyue Su; Jianzhou Wang; Haiyan Lu; Ge Zhao

2014-01-01T23:59:59.000Z

394

Automated Demand Response and Commissioning  

SciTech Connect (OSTI)

This paper describes the results from the second season of research to develop and evaluate the performance of new Automated Demand Response (Auto-DR) hardware and software technology in large facilities. Demand Response (DR) is a set of activities to reduce or shift electricity use to improve the electric grid reliability and manage electricity costs. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. We refer to this as Auto-DR. The evaluation of the control and communications must be properly configured and pass through a set of test stages: Readiness, Approval, Price Client/Price Server Communication, Internet Gateway/Internet Relay Communication, Control of Equipment, and DR Shed Effectiveness. New commissioning tests are needed for such systems to improve connecting demand responsive building systems to the electric grid demand response systems.

Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

2005-04-01T23:59:59.000Z

395

Physically-based demand modeling  

E-Print Network [OSTI]

for d1fferent values of insulation or control tempera- ture. Also, the results of var1ous load management. scenarios may be evaluated. 26 REFERENCES LZ] D. P. Lijesen and J. Rosing, MAdaptive Forecasting of Hourly Loads Based on Load Measurement...) Terry Marshall Calloway, B. S, , Northeast Louisiana University B. S. , Louisiana Tech University Chairman of Advisory Committee: Dr. C. W. Brice, III This thesis proposes a new methodology for modeling short-term (one hour to one day) air...

Calloway, Terry Marshall

1980-01-01T23:59:59.000Z

396

Demand Activated Manufacturing Architecture  

SciTech Connect (OSTI)

Honeywell Federal Manufacturing & Technologies (FM&T) engineers John Zimmerman and Tom Bender directed separate projects within this CRADA. This Project Accomplishments Summary contains their reports independently. Zimmerman: In 1998 Honeywell FM&T partnered with the Demand Activated Manufacturing Architecture (DAMA) Cooperative Business Management Program to pilot the Supply Chain Integration Planning Prototype (SCIP). At the time, FM&T was developing an enterprise-wide supply chain management prototype called the Integrated Programmatic Scheduling System (IPSS) to improve the DOE's Nuclear Weapons Complex (NWC) supply chain. In the CRADA partnership, FM&T provided the IPSS technical and business infrastructure as a test bed for SCIP technology, and this would provide FM&T the opportunity to evaluate SCIP as the central schedule engine and decision support tool for IPSS. FM&T agreed to do the bulk of the work for piloting SCIP. In support of that aim, DAMA needed specific DOE Defense Programs opportunities to prove the value of its supply chain architecture and tools. In this partnership, FM&T teamed with Sandia National Labs (SNL), Division 6534, the other DAMA partner and developer of SCIP. FM&T tested SCIP in 1998 and 1999. Testing ended in 1999 when DAMA CRADA funding for FM&T ceased. Before entering the partnership, FM&T discovered that the DAMA SCIP technology had an array of applications in strategic, tactical, and operational planning and scheduling. At the time, FM&T planned to improve its supply chain performance by modernizing the NWC-wide planning and scheduling business processes and tools. The modernization took the form of a distributed client-server planning and scheduling system (IPSS) for planners and schedulers to use throughout the NWC on desktops through an off-the-shelf WEB browser. The planning and scheduling process within the NWC then, and today, is a labor-intensive paper-based method that plans and schedules more than 8,000 shipped parts per month based on more than 50 manually-created document types. The fact that DAMA and FM&T desired to move from paper-based manual architectures to digitally based computer architectures gave further incentive for the partnership to grow. FM&T's greatest strength was its knowledge of NWC-wide scheduling and planning with its role as the NWC leader in manufacturing logistics. DAMA's asset was its new knowledge gained in the research and development of advanced architectures and tools for supply chain management in the textiles industry. These complimentary strengths allowed the two parties to provide both the context and the tools for the pilot. Bender: Honeywell FM&T participated in a four-site supply chain project, also referred to as an Inter-Enterprise Pipeline Evaluation. The MSAD project was selected because it involves four NWC sites: FM&T, Pantex, Los Alamos National Laboratory (LANL), and Lawrence Livermore National Laboratory (LLNL). FM&T had previously participated with Los Alamos National Laboratory in FY98 to model a two-site supply chain project, between FM&T and LANL. Evaluation of a Supply Chain Methodology is a subset of the DAMA project for the AMTEX consortium. LANL organization TSA-7, Enterprise Modeling and Simulation, has been involved in AMTEX and DAMA through development of process models and simulations for LANL, the NWC, and others. The FY 1998 and this FY 1999 projects directly involved collaboration between Honeywell and the Enterprise Modeling and Simulation (TSA-7) and Detonation Science and Technology (DX1) organizations at LANL.

Bender, T.R.; Zimmerman, J.J.

2001-02-07T23:59:59.000Z

397

Energy Demand Modelling Introduction to the PhD project  

E-Print Network [OSTI]

Energy Demand Modelling Introduction to the PhD project Erika Zvingilaite Risø DTU System Analysis for optimization of energy systems Environmental effects Global externalities cost of CO2 Future scenarios for the Nordic energy systems 2010, 2020, 2030, 2040, 2050 (energy-production, consumption, emissions, net costs

398

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

This report, which updates WHC-EP-0567, 1992 Solid Waste Reference Forecast Summary, (WHC 1992) forecasts the volumes of solid wastes to be generated or received at the US Department of Energy Hanford Site during the 30-year period from FY 1993 through FY 2022. The data used in this document were collected from Westinghouse Hanford Company forecasts as well as from surveys of waste generators at other US Department of Energy sites who are now shipping or plan to ship solid wastes to the Hanford Site for disposal. These wastes include low-level and low-level mixed waste, transuranic and transuranic mixed waste, and nonradioactive hazardous waste.

Valero, O.J.; Blackburn, C.L. [Westinghouse Hanford Co., Richland, WA (United States); Kaae, P.S.; Armacost, L.L.; Garrett, S.M.K. [Pacific Northwest Lab., Richland, WA (United States)

1993-08-01T23:59:59.000Z

399

Metrics for Evaluating the Accuracy of Solar Power Forecasting (Presentation)  

SciTech Connect (OSTI)

This presentation proposes a suite of metrics for evaluating the performance of solar power forecasting.

Zhang, J.; Hodge, B.; Florita, A.; Lu, S.; Hamann, H.; Banunarayanan, V.

2013-10-01T23:59:59.000Z

400

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

PSO (FU 2101) Ensemble-forecasts for wind power Analysis of the Results of an On-line Wind Power Ensemble- forecasts for wind power (FU2101) a demo-application producing quantile forecasts of wind power correct) quantile forecasts of the wind power production are generated by the application. However

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power study. Key words : wind power forecast, ramps, phase er- rors, forecasts ensemble. 1 Introduction Most

Boyer, Edmond

402

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

and number of countries withforeign subsidiaries) are significantly positively related to more optimistic management earnings forecasts....

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

403

Distribution of Wind Power Forecasting Errors from Operational Systems (Presentation)  

SciTech Connect (OSTI)

This presentation offers new data and statistical analysis of wind power forecasting errors in operational systems.

Hodge, B. M.; Ela, E.; Milligan, M.

2011-10-01T23:59:59.000Z

404

Demand and Price Uncertainty: Rational Habits in International Gasoline Demand  

E-Print Network [OSTI]

global gasoline and diesel price and income elasticities.shift in the short-run price elasticity of gasoline demand.Habits and Uncertain Relative Prices: Simulating Petrol Con-

Scott, K. Rebecca

2013-01-01T23:59:59.000Z

405

The Origins of Metropolitan Transportation Planning in Travel Demand Forecasting, 1944-1962  

E-Print Network [OSTI]

J. (1955). The law of retail gravitation applied to trafficas “Reilly’s Law of Retail Gravitation. ” Concepts like

Deutsch, Cheryl

2013-01-01T23:59:59.000Z

406

Demand Forecast Advisory Committee in Preparation for the Seventh Power Plan  

E-Print Network [OSTI]

products, electric motors, commercial water heaters, and heating, ventilation, and air conditioning and Ovens R Ai C diti Direct heating equipment Electric Motors Exit Signs General Service Fluorescent (HVAC) systems. EPAct also authorized DOE to develop of standards for products and directed DOE

407

Micro-simulation of daily activity-travel patterns for travel demand forecasting  

Science Journals Connector (OSTI)

The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day ...

Ryuichi Kitamura; Cynthia Chen; Ram M. Pendyala; Ravi Narayanan

408

MODELING THE DEMAND FOR E85 IN THE UNITED STATES  

SciTech Connect (OSTI)

How demand for E85 might evolve in the future in response to changing economics and policies is an important subject to include in the National Energy Modeling System (NEMS). This report summarizes a study to develop an E85 choice model for NEMS. Using the most recent data from the states of Minnesota, North Dakota, and Iowa, this study estimates a logit model that represents E85 choice as a function of prices of E10 and E85, as well as fuel availability of E85 relative to gasoline. Using more recent data than previous studies allows a better estimation of non-fleet demand and indicates that the price elasticity of E85 choice appears to be higher than previously estimated. Based on the results of the econometric analysis, a model for projecting E85 demand at the regional level is specified. In testing, the model produced plausible predictions of US E85 demand to 2040.

Liu, Changzheng [ORNL; Greene, David L [ORNL

2013-10-01T23:59:59.000Z

409

for the Future The Case for  

E-Print Network [OSTI]

Building for the Future The Case for Green Buildings and Energy Security for the University a contract from the Greenpeace Clean Energy Now! campaign. Building for the Future: The Case for Green and growing demand for renewable energy, energy efficiency, and green building practices from a wide range

California at Berkeley, University of

410

Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting  

E-Print Network [OSTI]

J.B. , 2004: Probabilistic wind power forecasts using localforecast intervals for wind power output using NWP-predictedsources such as wind and solar power. Integration of this

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

411

Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting  

E-Print Network [OSTI]

United States California Solar Initiative Coastally Trappedparticipants in the California Solar Initiative (CSI)on location. In California, solar irradiance forecasts near

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

412

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Download Adobe Acrobat Reader Printer friendly version on our site are provided in Adobe Acrobat Spreadsheets are provided in Excel Actual vs. Forecasts Formats Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF Table 12. World Oil Prices Excel, PDF Table 13. Natural Gas Wellhead Prices

413

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) MS Excel Viewer PDF (Acrobat Reader required Download Acrobat Reader ) Adobe Acrobat Reader Logo Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF

414

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

2004 2004 * The Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) has produced annual evaluations of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and replacing the historical year of data with the most recent. The forecast evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute percent errors for several of the major variables for AEO82 through AEO2004. (There is no report titled Annual Energy Outlook 1988 due to a change in the naming convention of the AEOs.) The average absolute percent error is the simple mean of the absolute values of the percentage difference between the Reference Case projection and the

415

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Economic Growth World Oil Prices Total Energy Consumption Residential and Commercial Sectors Industrial Sector Transportation Sector Electricity Natural Gas Petroleum Coal Three other organizations—Standard & Poor’s DRI (DRI), the WEFA Group (WEFA), and the Gas Research Institute (GRI) [95]—also produce comprehensive energy projections with a time horizon similar to that of AEO2001. The most recent projections from those organizations (DRI, Spring/Summer 2000; WEFA, 1st Quarter 2000; GRI, January 2000), as well as other forecasts that concentrate on petroleum, natural gas, and international oil markets, are compared here with the AEO2001 projections. Economic Growth Differences in long-run economic forecasts can be traced primarily to

416

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

energy data + forecasting energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. Links: FRED beta demo energy data + forecasting Syndicate content 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2084382122

417

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

Zhu, Xinxin

2013-07-22T23:59:59.000Z

418

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network [OSTI]

Testing Competing High-Resolution Precipitation Forecasts Eric Gilleland Research Prediction Comparison Test D1 D2 D = D1 ­ D2 copyright NCAR 2013 Loss Differential Field #12;Spatial Prediction Comparison Test Introduced by Hering and Genton

Gilleland, Eric

419

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

The short-term forecasting of capital expenditure presents one of the most difficult problems ... reason is that year-to-year fluctuations in capital expenditure are extremely wide. Some simple methods which...

W. Gerstenberger

1977-01-01T23:59:59.000Z

420

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

In contrast to short and extended range forecasts, predictions for periods beyond 5 days use time-averaged, midtropospheric height fields as their primary guidance. As time ranges are increased to 3O- and 90-day outlooks, guidance increasingly ...

A. James Wagner

1989-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

Certain satellite interpretation techniques have proven quite useful in the heavy snow forecast process. Those considered best are briefly reviewed, and another technique is introduced. This new technique was found to be most valuable in cyclonic ...

Edward C. Johnston

1995-06-01T23:59:59.000Z

422

building demand | OpenEI  

Open Energy Info (EERE)

demand demand Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

423

Demand Response Research in Spain  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Demand Response Research in Spain Demand Response Research in Spain Speaker(s): Iñigo Cobelo Date: August 22, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Mary Ann Piette The Spanish power system is becoming increasingly difficult to operate. The peak load grows every year, and the permission to build new transmission and distribution infrastructures is difficult to obtain. In this scenario Demand Response can play an important role, and become a resource that could help network operators. The present deployment of demand response measures is small, but this situation however may change in the short term. The two main Spanish utilities and the transmission network operator are designing research projects in this field. All customer segments are targeted, and the research will lead to pilot installations and tests.

424

EIA - AEO2010 - Electricity Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Demand Electricity Demand Annual Energy Outlook 2010 with Projections to 2035 Electricity Demand Figure 69. U.S. electricity demand growth 1950-2035 Click to enlarge » Figure source and data excel logo Figure 60. Average annual U.S. retail electricity prices in three cases, 1970-2035 Click to enlarge » Figure source and data excel logo Figure 61. Electricity generation by fuel in three cases, 2008 and 2035 Click to enlarge » Figure source and data excel logo Figure 62. Electricity generation capacity additions by fuel type, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 63. Levelized electricity costs for new power plants, 2020 and 2035 Click to enlarge » Figure source and data excel logo Figure 64. Electricity generating capacity at U.S. nuclear power plants in three cases, 2008, 2020, and 2035

425

Full Rank Rational Demand Systems  

E-Print Network [OSTI]

as a nominal income full rank QES. R EFERENCES (A.84)S. G. Donald. “Inferring the Rank of a Matrix. ” Journal of97-102. . “A Demand System Rank Theorem. ” Econometrica 57 (

LaFrance, Jeffrey T; Pope, Rulon D.

2006-01-01T23:59:59.000Z

426

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

Abstract In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the daily Phelix index of the German and Austrian control area of the European Energy Exchange (???) wholesale electricity market. The forecast accuracy we achieved is 76.12% over a 200 day period.

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

427

Demand Response and Energy Efficiency  

E-Print Network [OSTI]

Demand Response & Energy Efficiency International Conference for Enhanced Building Operations ESL-IC-09-11-05 Proceedings of the Ninth International Conference for Enhanced Building Operations, Austin, Texas, November 17 - 19, 2009 2 ?Less than 5..., 2009 4 An Innovative Solution to Get the Ball Rolling ? Demand Response (DR) ? Monitoring Based Commissioning (MBCx) EnerNOC has a solution involving two complementary offerings. ESL-IC-09-11-05 Proceedings of the Ninth International Conference...

428

Demand Response Spinning Reserve Demonstration  

SciTech Connect (OSTI)

The Demand Response Spinning Reserve project is a pioneeringdemonstration of how existing utility load-management assets can providean important electricity system reliability resource known as spinningreserve. Using aggregated demand-side resources to provide spinningreserve will give grid operators at the California Independent SystemOperator (CAISO) and Southern California Edison (SCE) a powerful, newtool to improve system reliability, prevent rolling blackouts, and lowersystem operating costs.

Eto, Joseph H.; Nelson-Hoffman, Janine; Torres, Carlos; Hirth,Scott; Yinger, Bob; Kueck, John; Kirby, Brendan; Bernier, Clark; Wright,Roger; Barat, A.; Watson, David S.

2007-05-01T23:59:59.000Z

429

Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000  

SciTech Connect (OSTI)

The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

Das, S.

1991-12-01T23:59:59.000Z

430

IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014 861 An Optimal and Distributed Demand Response  

E-Print Network [OSTI]

of demand response management for the future smart grid that integrates plug-in electric vehicles for augmented Lagrangian. I. INTRODUCTION I N THE electricity market, demand response [1] is a mech- anism to manage users' consumption behavior under spe- cific supply conditions. The goal of demand response

Nehorai, Arye

431

Addressing an Uncertain Future Using Scenario Analysis  

SciTech Connect (OSTI)

The Office of Energy Efficiency and Renewable Energy (EERE) has had a longstanding goal of introducing uncertainty into the analysis it routinely conducts in compliance with the Government Performance and Results Act (GPRA) and for strategic management purposes. The need to introduce some treatment of uncertainty arises both because it would be good general management practice, and because intuitively many of the technologies under development by EERE have a considerable advantage in an uncertain world. For example, an expected kWh output from a wind generator in a future year, which is not exposed to volatile and unpredictable fuel prices, should be truly worth more than an equivalent kWh from an alternative fossil fuel fired technology. Indeed, analysts have attempted to measure this value by comparing the prices observed in fixed-price natural gas contracts compared to ones in which buyers are exposed to market prices (see Bolinger, Wiser, and Golove and (2004)). In addition to the routine reasons for exploring uncertainty given above, the history of energy markets appears to have exhibited infrequent, but troubling, regime shifts, i.e., historic turning points at which the center of gravity or fundamental nature of the system appears to have abruptly shifted. Figure 1 below shows an estimate of how the history of natural gas fired generating costs has evolved over the last three decades. The costs shown incorporate both the well-head gas price and an estimate of how improving generation technology has gradually tended to lower costs. The purpose of this paper is to explore scenario analysis as a method for introducing uncertainty into EERE's forecasting in a manner consistent with the preceding observation. The two questions are how could it be done, and what is its academic basis, if any. Despite the interest in uncertainty methods, applying them poses some major hurdles because of the heavy reliance of EERE on forecasting tools that are deterministic in nature, such as the Energy Information Administration's (EIA's) National Energy Modeling System (NEMS). NEMS is the source of the influential Annual Energy Outlook whose business-as-usual (BAU) case, the Reference Case, forms the baseline for most of the U.S. energy policy discussion. NEMS is an optimizing model because: 1. it iterates to an equilibrium among modules representing the supply, demand, and energy conversion subsectors; and 2. several subsectoral models are individually solved using linear programs (LP). Consequently, it is deeply rooted in the recent past and any effort to simulate the consequences of a major regime shift as depicted in Figure 1 must come by applying an exogenously specified scenario. And, more generally, simulating futures that lie outside of our recent historic experience, even if they do not include regime switches suggest some form of scenario approach. At the same time, the statistical validity of scenarios that deviate significantly outside the ranges of historic inputs should be questioned.

Siddiqui, Afzal S.; Marnay, Chris

2006-12-15T23:59:59.000Z

432

National Action Plan on Demand Response  

Broader source: Energy.gov (indexed) [DOE]

Action Plan on Demand National Action Plan on Demand Action Plan on Demand National Action Plan on Demand Response Response Federal Utilities Partnership Working Group Federal Utilities Partnership Working Group November 18, 2008 November 18, 2008 Daniel Gore Daniel Gore Office of Energy Market Regulation Office of Energy Market Regulation Federal Energy Regulatory Commission Federal Energy Regulatory Commission The author's views do not necessarily represent the views of the Federal Energy Regulatory Commission Presentation Contents Presentation Contents Statutory Requirements Statutory Requirements National Assessment [Study] of Demand Response National Assessment [Study] of Demand Response National Action Plan on Demand Response National Action Plan on Demand Response General Discussion on Demand Response and Energy Outlook

433

Demand Response Projects: Technical and Market Demonstrations  

E-Print Network [OSTI]

Demand Response Projects: Technical and Market Demonstrations Philip D. Lusk Deputy Director Energy Analyst #12;PLACE CAPTION HERE. #12;#12;#12;#12;City of Port Angeles Demand Response History energy charges · Demand charges during peak period only ­ Reduced demand charges for demand response

434

Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services  

E-Print Network [OSTI]

A. Barat, D. Watson. 2006 Demand Response Spinning ReserveKueck, and B. Kirby 2008. Demand Response Spinning ReserveReport 2009. Open Automated Demand Response Communications

Kiliccote, Sila

2010-01-01T23:59:59.000Z

435

Demand Response and Open Automated Demand Response Opportunities for Data Centers  

E-Print Network [OSTI]

Standardized Automated Demand Response Signals. Presented atand Automated Demand Response in Industrial RefrigeratedActions for Industrial Demand Response in California. LBNL-

Mares, K.C.

2010-01-01T23:59:59.000Z

436

Home Network Technologies and Automating Demand Response  

SciTech Connect (OSTI)

Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively sophisticated energy consumers, it has been possible to improve the DR 'state of the art' with a manageable commitment of technical resources on both the utility and consumer side. Although numerous C & I DR applications of a DRAS infrastructure are still in either prototype or early production phases, these early attempts at automating DR have been notably successful for both utilities and C & I customers. Several factors have strongly contributed to this success and will be discussed below. These successes have motivated utilities and regulators to look closely at how DR programs can be expanded to encompass the remaining (roughly) half of the state's energy load - the light commercial and, in numerical terms, the more important residential customer market. This survey examines technical issues facing the implementation of automated DR in the residential environment. In particular, we will look at the potential role of home automation networks in implementing wide-scale DR systems that communicate directly to individual residences.

McParland, Charles

2009-12-01T23:59:59.000Z

437

Smart finite state devices: A modeling framework for demand response technologies  

E-Print Network [OSTI]

We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the ...

Turitsyn, Konstantin

438

Report: Natural Gas Infrastructure Implications of Increased Demand from the Electric Power Sector  

Broader source: Energy.gov [DOE]

This report examines the potential infrastructure needs of the U.S. interstate natural gas pipeline transmission system across a range of future natural gas demand scenarios that drive increased electric power sector natural gas use.

439

Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting  

Science Journals Connector (OSTI)

......optimum value through a grid-search algorithm...method outperformed TD for estimating the aggregate data series...variable, there is no benefit of forecasting each subaggregate...forecasting strategies in estimating the `component'-level...WILLEMAIN, T. R., SMART, C. N., SHOCKOR......

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

440

Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint  

SciTech Connect (OSTI)

The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

Zhang, J.; Florita, A.; Hodge, B. M.; Freedman, J.

2014-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

AN APPLICATION OF URBANSIM TO THE AUSTIN, TEXAS REGION: INTEGRATED-MODEL FORECASTS FOR THE YEAR 2030  

E-Print Network [OSTI]

AN APPLICATION OF URBANSIM TO THE AUSTIN, TEXAS REGION: INTEGRATED-MODEL FORECASTS FOR THE YEAR, as well as energy consumption and greenhouse gas emissions. This work describes the modeling of year-2030 policies significantly impact the region's future land use patterns, traffic conditions, greenhouse gas

Kockelman, Kara M.

442

Projecting household energy consumption within a conditional demand framework  

SciTech Connect (OSTI)

Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

Teotia, A.; Poyer, D.

1991-12-31T23:59:59.000Z

443

Projecting household energy consumption within a conditional demand framework  

SciTech Connect (OSTI)

Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

Teotia, A.; Poyer, D.

1991-01-01T23:59:59.000Z

444

Industrial demand side management: A status report  

SciTech Connect (OSTI)

This report provides an overview of and rationale for industrial demand side management (DSM) programs. Benefits and barriers are described, and data from the Manufacturing Energy Consumption Survey are used to estimate potential energy savings in kilowatt hours. The report presents types and examples of programs and explores elements of successful programs. Two in-depth case studies (from Boise Cascade and Eli Lilly and Company) illustrate two types of effective DSM programs. Interviews with staff from state public utility commissions indicate the current thinking about the status and future of industrial DSM programs. A comprehensive bibliography is included, technical assistance programs are listed and described, and a methodology for evaluating potential or actual savings from projects is delineated.

Hopkins, M.F.; Conger, R.L.; Foley, T.J. [and others

1995-05-01T23:59:59.000Z

445

Evaluating alternative fuels in USA: a proposed forecasting framework using AHP and scenarios  

Science Journals Connector (OSTI)

This paper proposes a forecasting framework that integrates the analytic hierarchy process with scenario analysis techniques to explore the commercialisation of future motor fuel technologies. We analyse the reasons for the uncertainty of oil price and how it affects alternative fuel commercialisation. We propose a set of evaluation criteria including Economic, Cultural, Environmental, Sustainability and Development Time. Finally, we develop four different Scenarios to verify the robustness of each alternative.

M.R. Nava; Tugrul U. Daim

2007-01-01T23:59:59.000Z

446

Facilitating Renewable Integration by Demand Response  

Science Journals Connector (OSTI)

Demand response is seen as one of the resources ... expected to incentivize small consumers to participate in demand response. This chapter models the involvement of small consumers in demand response programs wi...

Juan M. Morales; Antonio J. Conejo…

2014-01-01T23:59:59.000Z

447

Demand Response as a System Reliability Resource  

E-Print Network [OSTI]

Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning ReserveFormat of 2009-2011 Demand Response Activity Applications.

Joseph, Eto

2014-01-01T23:59:59.000Z

448

Demand response-enabled residential thermostat controls.  

E-Print Network [OSTI]

human dimension of demand response technology from a caseArens, E. , et al. 2008. Demand Response Enabling TechnologyArens, E. , et al. 2006. Demand Response Enabling Technology

Chen, Xue; Jang, Jaehwi; Auslander, David M.; Peffer, Therese; Arens, Edward A

2008-01-01T23:59:59.000Z

449

Value of Demand Response -Introduction Klaus Skytte  

E-Print Network [OSTI]

Value of Demand Response - Introduction Klaus Skytte Systems Analysis Department February 7, 2006 Energinet.dk, Ballerup #12;What is Demand Response? Demand response (DR) is the short-term response

450

Balancing of Energy Supply and Residential Demand  

Science Journals Connector (OSTI)

Power demand of private households shows daily fluctuations and ... (BEV) and heat pumps. This additional demand, especially when it remains unmanaged, will ... to an increase in fluctuations. To balance demand,

Martin Bock; Grit Walther

2014-01-01T23:59:59.000Z

451

Industrial demand side management status report: Synopsis  

SciTech Connect (OSTI)

Industrial demand side management (DSM) programs, though not as developed or widely implemented as residential and commercial programs, hold the promise of significant energy savings-savings that will benefit industrial firms, utilities and the environment. This paper is a synopsis of a larger research report, Industrial Demand Side Management. A Status Report, prepared for the US Department of Energy. The report provides an overview of and rationale for DSM programs. Benefits and barriers are described, and data from the Manufacturing Energy Consumption Survey are used to estimate potential electricity savings from industrial energy efficiency measures. Overcoming difficulties to effective program implementation is worthwhile, since rough estimates indicate a substantial potential for electricity savings. The report categorizes types of DSM programs, presents several examples of each type, and explores elements of successful programs. Two in-depth case studies (of Boise Cascade and of Eli Lilly and Company) illustrate two types of effective DSM programs. Interviews with staff from state public utility commissions indicate the current thinking about the status and future of industrial DSM programs. Finally, the research report also includes a comprehensive bibliography, a description of technical assistance programs, and an example of a methodology for evaluating potential or actual savings from projects.

Hopkins, M.E.F.; Conger, R.L.; Foley, T.J.; Parker, J.W.; Placet, M.; Sandahl, L.J.; Spanner, G.E.; Woodruff, M.G.; Norland, D.

1995-08-01T23:59:59.000Z

452

Definition: Demand | Open Energy Information  

Open Energy Info (EERE)

form form View source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Definition Edit with form History Facebook icon Twitter icon » Definition: Demand Jump to: navigation, search Dictionary.png Demand The rate at which electric energy is delivered to or by a system or part of a system, generally expressed in kilowatts or megawatts, at a given instant or averaged over any designated interval of time., The rate at which energy is being used by the customer.[1] Related Terms energy, electricity generation References ↑ Glossary of Terms Used in Reliability Standards An i Like Like You like this.Sign Up to see what your friends like. nline Glossary Definition Retrieved from "http://en.openei.org/w/index.php?title=Definition:Demand&oldid=480555"

453

Winter Demand Impacted by Weather  

Gasoline and Diesel Fuel Update (EIA)

8 8 Notes: Heating oil demand is strongly influenced by weather. The "normal" numbers are the expected values for winter 2000-2001 used in EIA's Short-Term Energy Outlook. The chart indicates the extent to which the last winter exhibited below-normal heating degree-days (and thus below-normal heating demand). Temperatures were consistently warmer than normal throughout the 1999-2000 heating season. This was particularly true in November 1999, February 2001 and March 2001. For the heating season as a whole (October through March), the 1999-2000 winter yielded total HDDs 10.7% below normal. Normal temperatures this coming winter would, then, be expected to bring about 11% higher heating demand than we saw last year. Relative to normal, the 1999-2000 heating season was the warmest in

454

Turkey's energy demand and supply  

SciTech Connect (OSTI)

The aim of the present article is to investigate Turkey's energy demand and the contribution of domestic energy sources to energy consumption. Turkey, the 17th largest economy in the world, is an emerging country with a buoyant economy challenged by a growing demand for energy. Turkey's energy consumption has grown and will continue to grow along with its economy. Turkey's energy consumption is high, but its domestic primary energy sources are oil and natural gas reserves and their production is low. Total primary energy production met about 27% of the total primary energy demand in 2005. Oil has the biggest share in total primary energy consumption. Lignite has the biggest share in Turkey's primary energy production at 45%. Domestic production should be to be nearly doubled by 2010, mainly in coal (lignite), which, at present, accounts for almost half of the total energy production. The hydropower should also increase two-fold over the same period.

Balat, M. [Sila Science, Trabzon (Turkey)

2009-07-01T23:59:59.000Z

455

Microsoft Word - Documentation - Price Forecast Uncertainty.doc  

U.S. Energy Information Administration (EIA) Indexed Site

October 2009 October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy- related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the market- clearing process for risk transfer can be used to generate "price bands" around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to

456

EIA - Assumptions to the Annual Energy Outlook 2009 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2009 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the projection horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the projection horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

457

Demand Response as a System Reliability Resource  

E-Print Network [OSTI]

for Demand Response Technology Development The objective ofin planning demand response technology RD&D by conductingNew and Emerging Technologies into the California Smart Grid

Joseph, Eto

2014-01-01T23:59:59.000Z

458

Coordination of Energy Efficiency and Demand Response  

E-Print Network [OSTI]

California Long-term Energy Efficiency Strategic Plan. B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Response

Goldman, Charles

2010-01-01T23:59:59.000Z

459

Demand Response - Policy | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

Demand Response - Policy Demand Response - Policy Since its inception, the Office of Electricity Delivery and Energy Reliability (OE) has been committed to modernizing the nation's...

460

Sandia National Laboratories: demand response inverter  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

demand response inverter ECIS-Princeton Power Systems, Inc.: Demand Response Inverter On March 19, 2013, in DETL, Distribution Grid Integration, Energy, Energy Surety, Facilities,...

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Coordination of Energy Efficiency and Demand Response  

E-Print Network [OSTI]

and Demand Response A pilot program from NSTAR in Massachusetts,Massachusetts, aiming to test whether an intensive program of energy efficiency and demand response

Goldman, Charles

2010-01-01T23:59:59.000Z

462

California Energy Demand Scenario Projections to 2050  

E-Print Network [OSTI]

annual per-capita electricity consumption by demand15 California electricity consumption projections by demandannual per-capita electricity consumption by demand

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

463

Marketing & Driving Demand: Social Media Tools & Strategies ...  

Broader source: Energy.gov (indexed) [DOE]

Demand: Social Media Tools & Strategies - January 16, 2011 Marketing & Driving Demand: Social Media Tools & Strategies - January 16, 2011 January 16, 2011 Conference Call...

464

Marketing & Driving Demand Collaborative - Social Media Tools...  

Broader source: Energy.gov (indexed) [DOE]

Demand Collaborative - Social Media Tools & Strategies Marketing & Driving Demand Collaborative - Social Media Tools & Strategies Presentation slides from the BetterBuildings...

465

California Energy Demand Scenario Projections to 2050  

E-Print Network [OSTI]

Vehicle Conventional and Alternative Fuel Response Simulatormodified to include alternative fuel demand scenarios (whichvehicle adoption and alternative fuel demand) later in the

McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

2008-01-01T23:59:59.000Z

466

Radar-Derived Forecasts of Cloud-to-Ground Lightning Over Houston, Texas  

E-Print Network [OSTI]

Lightning Forecasts..........................................................................................45 2.7 First Flash Forecasts and Lead Times.....................................................................47 vii... Cell Number ? 25 August 2000..............................................68 3.4 First Flash Forecast Time........................................................................................70 3.5 Lightning Forecasting Algorithm (LFA) Development...

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

467

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) PDF (Acrobat Reader required) Table 2. Total Energy Consumption HTML, Excel, PDF Table 3. Total Petroleum Consumption HTML, Excel, PDF Table 4. Total Natural Gas Consumption HTML, Excel, PDF Table 5. Total Coal Consumption HTML, Excel, PDF Table 6. Total Electricity Sales HTML, Excel, PDF Table 7. Crude Oil Production HTML, Excel, PDF Table 8. Natural Gas Production HTML, Excel, PDF Table 9. Coal Production HTML, Excel, PDF Table 10. Net Petroleum Imports HTML, Excel, PDF Table 11. Net Natural Gas Imports HTML, Excel, PDF Table 12. Net Coal Exports HTML, Excel, PDF Table 13. World Oil Prices HTML, Excel, PDF

468

Smart Buildings and Demand Response  

Science Journals Connector (OSTI)

Advances in communications and control technology the strengthening of the Internet and the growing appreciation of the urgency to reduce demand side energy use are motivating the development of improvements in both energy efficiency and demand response (DR) systems in buildings. This paper provides a framework linking continuous energy management and continuous communications for automated demand response (Auto?DR) in various times scales. We provide a set of concepts for monitoring and controls linked to standards and procedures such as Open Automation Demand Response Communication Standards (OpenADR). Basic building energy science and control issues in this approach begin with key building components systems end?uses and whole building energy performance metrics. The paper presents a framework about when energy is used levels of services by energy using systems granularity of control and speed of telemetry. DR when defined as a discrete event requires a different set of building service levels than daily operations. We provide examples of lessons from DR case studies and links to energy efficiency.

2011-01-01T23:59:59.000Z

469

Water demand management in Kuwait  

E-Print Network [OSTI]

Kuwait is an arid country located in the Middle East, with limited access to water resources. Yet water demand per capita is much higher than in other countries in the world, estimated to be around 450 L/capita/day. There ...

Milutinovic, Milan, M. Eng. Massachusetts Institute of Technology

2006-01-01T23:59:59.000Z

470

A Unit Commitment Model with Demand Response for the Integration of Renewable Energies  

E-Print Network [OSTI]

The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast of output its error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms and analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increases with the forecast error. The effect of a sudden decrease in wind power is also analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total opera...

Ikeda, Yuichi; Kataoka, Kazuto; Ogimoto, Kazuhiko

2011-01-01T23:59:59.000Z

471

12-32021E2_Forecast  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

FORECAST OF VACANCIES FORECAST OF VACANCIES Until end of 2014 (Issue No. 20) Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff at the P4-P5 levels: * Advanced university degree (or equivalent postgraduate degree); * 7 or 10 years, respectively, of experience in a field of relevance to the post; * Resource management experience; * Strong analytical skills; * Computer skills: standard Microsoft Office software; * Languages: Fluency in English. Working knowledge of other official languages (Arabic, Chinese, French, Russian, Spanish) advantageous; * Ability to work effectively in multidisciplinary and multicultural teams; * Ability to communicate effectively. Professional staff at the P1-P3 levels:

472

Building Energy Software Tools Directory: Degree Day Forecasts  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Forecasts Forecasts Degree Day Forecasts example chart Quick and easy web-based tool that provides free 14-day ahead degree day forecasts for 1,200 stations in the U.S. and Canada. Degree Day Forecasts charts show this year, last year and three-year average. Historical degree day charts and energy usage forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700. Expertise Required No special expertise required. Simple to use. Users Over 1,000 weekly users. Audience Anyone who needs degree day forecasts (next 14 days) for the U.S. and Canada. Input Select a weather station (1,200 available) and balance point temperature. Output Charts show (1) degree day (heating and cooling) forecasts for the next 14

473

Building Energy Software Tools Directory: Energy Usage Forecasts  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Energy Usage Forecasts Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides free 14-day ahead energy usage forecasts based on the degree day forecasts for 1,200 stations in the U.S. and Canada. The user enters the daily non-weather base load and the usage per degree day weather factor; the tool applies the degree day forecast and displays the total energy usage forecast. Helpful FAQs explain the process and describe various options for the calculation of the base load and weather factor. Historical degree day reports and 14-day ahead degree day forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature, load calculation, energy simulation Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700.

474

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

Managing water resources generally and managing reservoir operations specifically have been touted as opportunities for applying forecasts to improve decision making. Previous studies have shown that the application of forecasts into water ...

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

475

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

This project applies Data Mining technology to the prediction of electric power system load forecast. It proposes a mining program of electric power load forecasting data based on the similarity of time series .....

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao…

2013-01-01T23:59:59.000Z

476

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

An integrated hydrometeorological system was designed for the utilization of data from various sensors in the 3300 km2 Panama Canal Watershed for the purpose of producing ... forecasts. These forecasts are used b...

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

477

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode ... is proposed in this paper. Firstly,...

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

478

Beyond "Partly Sunny": A Better Solar Forecast | Department of...  

Energy Savers [EERE]

Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar...

479

Customer Demand Issues in SmartGrids European Platform: Relevant  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Customer Demand Issues in SmartGrids European Platform: Relevant Customer Demand Issues in SmartGrids European Platform: Relevant Initiatives Speaker(s): Carlos Alvarez-Bel Date: June 26, 2012 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Mary Ann Piette SmartGrids technological platform was created by the European Commission in order to develop and identify research topics and objectives to facilitate the implementation of future electric grids. Smart grid is, by definition, user-centric, which implies that enhancing and promoting customer participation in electricity markets and systems, from efficiency to demand response, is a key goal. Efficiency targets in Europe (20% energy reduction in 2020) will probably not be met and, on the contrary, the renewable generation share target of 20% for the same year seems affordable. These

480

Water supply and demand in an energy supply model  

SciTech Connect (OSTI)

This report describes a tool for water and energy-related policy analysis, the development of a water supply and demand sector in a linear programming model of energy supply in the United States. The model allows adjustments in the input mix and plant siting in response to water scarcity. Thus, on the demand side energy conversion facilities can substitute more costly dry cooling systems for conventional evaporative systems. On the supply side groundwater and water purchased from irrigators are available as more costly alternatives to unappropriated surface water. Water supply data is developed for 30 regions in 10 Western states. Preliminary results for a 1990 energy demand scenario suggest that, at this level of spatial analysis, water availability plays a minor role in plant siting. Future policy applications of the modeling system are discussed including the evaluation of alternative patterns of synthetic fuels development.

Abbey, D; Loose, V

1980-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast future demand" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Electric-utility DSM programs: 1990 data and forecasts to 2000  

SciTech Connect (OSTI)

In April 1992, the Energy Information Administration (EIA) released data on 1989 and 1990 electric-utility demand-site management (DMS) programs. These data represent a census of US utility DSM programs, with reports of utility expenditures, energy savings, and load reductions caused by these programs. In addition, EIA published utility estimates of the costs and effects of these programs from 1991 to 2000. These data provide the first comprehensive picture of what utilities are spending and accomplishing by utility, state, and region. This report presents, summarizes, and interprets the 1990 data and the utility forecasts of their DSM-program expenditures and impacts to the year 2000. Only utilities with annual sales greater than 120 GWh were required to report data on their DSM programs to EIA. Of the 1194 such utilities, 363 reported having a DSM program that year. These 363 electric utilities spent $1.2 billion on their DSM programs in 1990, up from $0.9 billion in 1989. Estimates of energy savings (17,100 GWh in 1990 and 14,800 GWh in 1989) and potential reductions in peak demand (24,400 MW in 1990 and about 19,400 MW in 1989) also showed substantial increases. Overall, utility DSM expenditures accounted for 0.7% of total US electric revenues, while the reductions in energy and demand accounted for 0.6% and 4.9% of their respective 1990 national totals. The investor-owned utilities accounted for 70 to 90% of the totals for DSM costs, energy savings, and demand reductions. The public utilities reported larger percentage reductions in peak demand and energy smaller percentage DSM expenditures. These averages hide tremendous variations across utilities. Utility forecasts of DSM expenditures and effects show substantial growth in both absolute and relative terms.

Hirst, E.

1992-06-01T23:59:59.000Z

482

Real time voltage control using emergency demand response in distribution system by integrating advanced metering infrastructure  

Science Journals Connector (OSTI)

In this paper an analytical study is reported to demonstrate the effects of demand response on distribution network voltages profile. Also a new approach for real time voltage control is proposed which uses emergency demand response program aiming at maintaining voltage profile in an acceptable range with minimum cost. This approach will be active in emergency conditions where in real time the voltages in some nodes leave their permissible ranges. These emergency conditions are Distributed Generation (DG) units and lines outage and unpredictable demand and renewable generations' fluctuations. The proposed approach does not need the load and renewable generation forecast data to regulate voltage. To verify the effectiveness and robustness of the proposed control scheme the proposed voltage control scheme is tested on a typical distribution network. The simulation results show the effectiveness and capability of the proposed real time voltage control model to maintain smart distribution network voltage in specified ranges in both normal and emergency conditions.

Alireza Zakariazadeh; Shahram Jadid

2014-01-01T23:59:59.000Z

483

The alchemy of demand response: turning demand into supply  

SciTech Connect (OSTI)

Paying customers to refrain from purchasing products they want seems to run counter to the normal operation of markets. Demand response should be interpreted not as a supply-side resource but as a secondary market that attempts to correct the misallocation of electricity among electric users caused by regulated average rate tariffs. In a world with costless metering, the DR solution results in inefficiency as measured by deadweight losses. (author)

Rochlin, Cliff

2009-11-15T23:59:59.000Z

484

Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services  

E-Print Network [OSTI]

shows how the actual load profile follows the hourly bidscriteria were as follows: Low load variability – enhancesloads, the actual loads do not closely follow the forecasted

Kiliccote, Sila

2010-01-01T23:59:59.000Z

485

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts.

Botterud, A.; Wang, J.; Miranda, V.; Bessa, R. J.; Decision and Information Sciences; INESC Porto

2010-04-01T23:59:59.000Z

486

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. (author)

Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro; Bessa, Ricardo J.

2010-04-15T23:59:59.000Z

487

Sandia National Laboratories: Solar Energy Forecasting and Resource...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

488

Q:\asufinal_0107_demand.vp  

Gasoline and Diesel Fuel Update (EIA)

00 00 (AEO2000) Assumptions to the January 2000 With Projections to 2020 DOE/EIA-0554(2000) Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Natural Gas Transmission and Distribution

489

Assessment of Demand Response and Advanced Metering  

E-Print Network [OSTI]

#12;#12;2008 Assessment of Demand Response and Advanced Metering Staff Report Federal Energy metering penetration and potential peak load reduction from demand response have increased since 2006. Significant activity to promote demand response or to remove barriers to demand response occurred at the state

Tesfatsion, Leigh

490

INTEGRATION OF PV IN DEMAND RESPONSE  

E-Print Network [OSTI]

INTEGRATION OF PV IN DEMAND RESPONSE PROGRAMS Prepared by Richard Perez et al. NREL subcontract response programs. This is because PV generation acts as a catalyst to demand response, markedly enhancing by solid evidence from three utility case studies. BACKGROUND Demand Response: demand response (DR

Perez, Richard R.

491

Demand Side Management in Rangan Banerjee  

E-Print Network [OSTI]

Demand Side Management in Industry Rangan Banerjee Talk at Baroda in Birla Corporate Seminar August 31,2007 #12;Demand Side Management Indian utilities ­ energy shortage and peak power shortage. Supply for Options ­ Demand Side Management (DSM) & Load Management #12;DSM Concept Demand Side Management (DSM) - co

Banerjee, Rangan

492

Building Technologies Office: Integrated Predictive Demand Response  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Integrated Predictive Integrated Predictive Demand Response Controller Research Project to someone by E-mail Share Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Facebook Tweet about Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Twitter Bookmark Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Google Bookmark Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Delicious Rank Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on Digg Find More places to share Building Technologies Office: Integrated Predictive Demand Response Controller Research Project on AddThis.com...

493

Market penetration: How to predict the future  

SciTech Connect (OSTI)

One of the biggest challenges in evaluating energy efficiency programs if predicting how customers will react to future changes in incentives and technology. This is especially true within a competitive energy market. This paper presents a market penetration model based on states preference experiments that is designed to address this issue. This model can be used to predict customer purchases under alternative market conditions such as changes in technology, program rebates, and program qualifying equipment. The states preference experiments elicit ratings from residential customers on program and equipment attributes such as price, rebate, and annual energy savings. The data were collected and the model estimated using information from Florida Power and Light's Residential HVAC Program. Once the data are gathered, a logic model is estimated to determine the probability that each program and equipment option is chosen. The model is calibrated to actual customer purchases and then used to predict future equipment purchases and program participation. When values for very high efficiency equipment are included in the experiment, the model can be used to forecast future purchases even when purchases of these units are not currently widespread. This market penetration model provides a method to forecast equipment purchases, while taking into account future changes in technology, and, as a result, will be valuable to any utility seeking to continue energy efficiency programs in a competitive market.

Grover, S.; Ikenze-Bates, I.

1998-07-01T23:59:59.000Z

494

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

Boyer, Edmond

495

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

PSO (FU 2101) Ensemble-forecasts for wind power Wind Power Ensemble Forecasting Using Wind Speed the problems of (i) transforming the meteorological ensembles to wind power ensembles and, (ii) correcting) data. However, quite often the actual wind power production is outside the range of ensemble forecast

496

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

· advantage: no NWP data necessary ­ very actual shortest term forecasts possible · wind power inputAccuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method

Heinemann, Detlev

497

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira Farka, Ph.D. -- Co-Director, Center for Economic Analysis and Forecasting -- Associate Professor

de Lijser, Peter

498

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

Forecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona; Numerical weather prediction 1. Introduction Wave forecasting is now an integral part of operational weather methods for generating such forecasts from numerical model output from the European Centre for Medium

Stevenson, Paul

499

Northwest Open Automated Demand Response Technology Demonstration Project  

E-Print Network [OSTI]

Report 2009. Open Automated Demand Response Communicationsand Techniques for Demand Response. California Energyand S. Kiliccote. Estimating Demand Response Load Impacts:

Kiliccote, Sila

2010-01-01T23:59:59.000Z

500

Incorporating Demand Response into Western Interconnection Transmission Planning  

E-Print Network [OSTI]

Aggregator Programs. Demand Response Measurement andIncorporating Demand Response into Western Interconnection13 Demand Response Dispatch

Satchwell, Andrew

2014-01-01T23:59:59.000Z