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We encourage you to perform a real-time search of NLEBeta
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1

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

2

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

3

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.

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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.

12

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.

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

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

15

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

16

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

17

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

18

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

19

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

20

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.

Note: This page contains sample records for the topic "demand module forecasts" 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

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

22

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...........................................................................................

23

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.

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

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.

33

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.

34

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

35

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

36

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.

37

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

38

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

39

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.

40

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

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

52

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

53

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

54

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

55

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

56

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

57

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

58

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

59

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates 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, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

60

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. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas, and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.

NONE

1998-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "demand module forecasts" 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

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

62

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

63

Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumption to the Annual Energy Outlook Industrial Demand Module Table 17. Industry Categories Printer Friendly Version Energy-Intensive Manufacturing Nonenergy-Intensive Manufacturing Nonmanufacturing Industries Food and Kindred Products (NAICS 311) Metals-Based Durables (NAICS 332-336) Agricultural Production -Crops (NAICS 111) Paper and Allied Products (NAICS 322) Balance of Manufacturing (all remaining manufacturing NAICS) Other Agriculture Including Livestock (NAICS112- 115) Bulk Chemicals (NAICS 32B) Coal Mining (NAICS 2121) Glass and Glass Products (NAICS 3272) Oil and Gas Extraction (NAICS 211) Hydraulic Cement (NAICS 32731) Metal and Other Nonmetallic Mining (NAICS 2122- 2123) Blast Furnaces and Basic Steel (NAICS 331111) Construction (NAICS233-235)

64

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

65

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

66

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

67

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

68

Model documentation report: Residential 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) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document providing a detailed description for energy analysts, other 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 according to 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.

NONE

1995-03-01T23:59:59.000Z

69

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

70

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

71

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

72

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

73

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

74

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

75

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

76

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

77

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

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2007 Transportation Demand Module The NEMS Transportation Demand Module estimates 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 isthe 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), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

78

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

79

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

80

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

82

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

83

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

84

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

85

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,

86

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

87

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

88

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

89

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

90

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

91

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

92

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

93

D:\assumptions_2001\assumptions2002\currentassump\demand.vp  

Gasoline and Diesel Fuel Update (EIA)

2 2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Petroleum Market Module. . . . . . . . . . . . .

94

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

95

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

96

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...

97

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.

98

Residential Demand Module  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

for EIA (SENTECH Incorporated, 2010). Wind: The Cost and Performance of Distributed Wind Turbines, 2010-35 (ICF International, 2010). 33 U.S. Energy Information Administration |...

99

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

100

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

102

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

103

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

104

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

105

Design, Implementation, and Formal Verification of On-demand Connection Establishment Scheme for TCP Module of MPICH2 Library  

E-Print Network [OSTI]

developed at Argonne National Laboratory. The scalability of MPI implementations is very important for building high performance parallel computing applications. The initial TCP (Transmission Control Protocol) network module developed for Nemesis...

Muthukrishnan, Sankara Subbiah

2012-10-19T23:59:59.000Z

106

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.

107

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

108

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

109

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

110

A Buildings Module for the Stochastic Energy Deployment System  

SciTech Connect (OSTI)

The U.S. Department of Energy (USDOE) is building a new long-range (to 2050) forecasting model for use in budgetary and management applications called the Stochastic Energy Deployment System (SEDS), which explicitly incorporates uncertainty through its development within the Analytica(R) platform of Lumina Decision Systems. SEDS is designed to be a fast running (a few minutes), user-friendly model that analysts can readily run and modify in its entirety through a visual programming interface. Lawrence Berkeley National Laboratory is responsible for implementing the SEDS Buildings Module. The initial Lite version of the module is complete and integrated with a shared code library for modeling demand-side technology choice developed by the National Renewable Energy Laboratory (NREL) and Lumina. The module covers both commercial and residential buildings at the U.S. national level using an econometric forecast of floorspace requirement and a model of building stock turnover as the basis for forecasting overall demand for building services. Although the module is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies, it differs from standard energy forecasting models by including considerations of passive building systems, interactions between technologies (such as internal heat gains), and on-site power generation.

Lacommare, Kristina S H; Marnay, Chris; Stadler, Michael; Borgeson, Sam; Coffey, Brian; Komiyama, Ryoichi; Lai, Judy

2008-05-15T23:59:59.000Z

111

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 23 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 International Energy Module The NEMS International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the NEMS IEM computes world oil prices, provides a supply curve of world crude-like liquids, generates a worldwide oil supply- demand balance with regional detail, and computes quantities of crude oil and light and heavy petroleum products imported into

112

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 International Energy Module The NEMS International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the NEMS IEM computes oil prices, provides a supply curve of world crude-like liquids, generates a worldwide oil supply- demand balance with regional detail, and computes quantities of crude oil and light and heavy petroleum products imported into the United States by export region. Changes in the oil price (WTI), which is defined as the price of light, low-sulfur crude oil delivered to Cushing, Oklahoma in

113

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

114

The National Energy Modeling System: An Overview 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply regions (Figures 19 and 20). coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply regions (Figures 19 and 20). Figure 19. Coal Market Module Demand Regions Figure 20. Coal Market Module Supply Regions

115

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

116

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...

117

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

118

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

119

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

120

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

122

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

123

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

124

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

125

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.

126

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.

127

The National Energy Modeling System: An Overview 1998 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

COAL MARKET MODULE COAL MARKET MODULE blueball.gif (205 bytes) Coal Production Submodule blueball.gif (205 bytes) Coal Distribution Submodule blueball.gif (205 bytes) Coal Export Component The coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. The CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply

128

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.

129

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

130

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

131

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

132

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

133

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

134

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

135

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...

136

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

137

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

138

Model documentation, Coal Market Module of the National Energy Modeling System  

SciTech Connect (OSTI)

This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

NONE

1998-01-01T23:59:59.000Z

139

Assumptions to the Annual Energy Outlook 2001 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

140

Assumptions to the Annual Energy Outlook 2002 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

Note: This page contains sample records for the topic "demand module forecasts" 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

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

142

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

143

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

144

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...

145

Microsoft PowerPoint - FinalModule6.ppt  

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

6: Metrics, Performance 6: Metrics, Performance Measurements and Forecasting Prepared by: Module 6 - Metrics, Performance Measures and Forecasting 2 Prepared by: Booz Allen Hamilton Module 6: Metrics, Performance Measurements and Forecasting Welcome to Module 6. The objective of this module is to introduce you to the Metrics and Performance Measurement tools used, along with Forecasting, in Earned Value Management. The Topics that will be addressed in this Module include: * Define Cost and Schedule Variances * Define Cost and Schedule Performance Indices * Define Estimate to Complete (ETC) * Define Estimate at Completion (EAC) and Latest Revised Estimate (LRE) Module 6 - Metrics, Performance Measures and Forecasting 3 Prepared by: Booz Allen Hamilton Review of Previous Modules Let's quickly review what has been covered in the previous modules.

146

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...

147

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

148

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

149

EIA-Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2007 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

150

Comparison of Bottom-Up and Top-Down Forecasts: Vision Industry Energy Forecasts with ITEMS and NEMS  

E-Print Network [OSTI]

of the Department of Energy's Office of Industrial Technologies, EIA extracted energy use infonnation from the Annual Energy Outlook (AEO) - 2000 (8) for each of the seven # The Pacific Northwest National Laboratory is operated by Battelle Memorial Institute...-6, 2000 NEMS The NEMS industrial module is the official forecasting model for EIA and thus the Department of Energy. For this reason, the energy prices and output forecasts used to drive the ITEMS model were taken from EIA's AEO 2000. Understanding...

Roop, J. M.; Dahowski, R. T

151

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

152

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

153

Assumptions to the Annual Energy Outlook 2000 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.100

154

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

155

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...

156

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

157

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

158

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,

159

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

160

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 "demand module forecasts" 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

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...

162

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

163

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

164

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 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 projects 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 projection using the SEDS1 data.

165

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,

166

EIA - Assumptions to the Annual Energy Outlook 2008 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2008 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. 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.1

167

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

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. 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.1

168

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

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 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 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 [1].

169

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

170

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.

171

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

172

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

173

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

174

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

175

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,

176

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach  

Science Journals Connector (OSTI)

Abstract A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both least squares support vector machine (LSSVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. The proposed hybrid model showed improved forecasting accuracy compared to a forecasting model using a single LSSVM.

Xing Yan; Nurul A. Chowdhury

2013-01-01T23:59:59.000Z

177

Solar in Demand | Department of Energy  

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

Solar in Demand Solar in Demand Solar in Demand June 15, 2012 - 10:23am Addthis Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin Donovan's town home. | Credit: Dennis Schroeder. Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin Donovan's town home. | Credit: Dennis Schroeder. April Saylor April Saylor Former Digital Outreach Strategist, Office of Public Affairs What does this mean for me? A new study says U.S. developers are likely to install about 3,300 megawatts of solar panels in 2012 -- almost twice the amount installed last year. In case you missed it... This week, the Wall Street Journal published an article, "U.S. Solar-Panel Demand Expected to Double," highlighting the successes of

178

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

179

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

180

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

Note: This page contains sample records for the topic "demand module forecasts" 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

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

he International Energy Module determines changes in the world oil price and the supply prices of crude he International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(06), (Washington, DC, February 2006).

182

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.

183

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

184

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

185

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

186

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.

187

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,

188

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

189

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

190

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

191

Electricity Market Module  

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

Market Module Market Module This page inTenTionally lefT blank 101 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, electricity load and demand, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2013, DOE/EIA-M068(2013). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most

192

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

193

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

194

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

195

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

196

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...

197

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.

198

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

199

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...

200

Assumptions to the Annual Energy Outlook 1999 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

petroleum.gif (4999 bytes) petroleum.gif (4999 bytes) The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below. 75

Note: This page contains sample records for the topic "demand module forecasts" 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

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

202

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

203

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

204

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

205

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...

206

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

207

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

208

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

209

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates 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), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

210

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

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates 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), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

211

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

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing 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

212

Price volatility forecasting using artificial neural networks in emerging electricity markets  

Science Journals Connector (OSTI)

In the adaptive short-term electricity price forecasting, it may be premature to rely solely on the hourly price forecast. The volatility of electricity price should also be analysed to provide additional insight on price forecasting. This paper proposes a price volatility module to analyse electricity price spikes and study the probability distribution of electricity price. Two methods are used to study the probability distribution of electricity price: the analytical method and the ANN method. Furthermore, ANN method is used to study the impact of line limits, line outages, generator outages, load pattern and bidding strategy on short term price forecasting, in addition to sensitivity analysis to determine the extent to which these factors impact price forecasting. Data used in this study are spot electricity prices from California market in the period which includes the crisis months where extreme volatility was observed.

Ahmad F. Al-Ajlouni; Hatim Y. Yamin; Ali Eyadeh

2012-01-01T23:59:59.000Z

213

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.

214

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

215

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

216

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

217

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

218

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

219

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

220

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

222

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":""}]}

223

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

224

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

225

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

226

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

227

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

228

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

229

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...

230

Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

This page inTenTionally lefT blank 91 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, electricity load and demand, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2012, DOE/EIA-M068(2012). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most

231

Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 95 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, electricity load and demand, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2011, DOE/EIA-M068(2011). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most

232

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

233

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

234

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

235

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

236

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

237

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"

238

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

239

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.

240

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

Note: This page contains sample records for the topic "demand module forecasts" 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

Model documentation: Renewable Fuels Module of the National Energy Modeling System  

SciTech Connect (OSTI)

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it related to the production of the 1994 Annual Energy Outlook (AEO94) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves two purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. The RFM consists of six analytical submodules that represent each of the major renewable energy resources -- wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. Of these six, four are documented in the following chapters: municipal solid waste, wind, solar and biofuels. Geothermal and wood are not currently working components of NEMS. The purpose of the RFM is to define the technological and cost characteristics of renewable energy technologies, and to pass these characteristics to other NEMS modules for the determination of mid-term forecasted renewable energy demand.

Not Available

1994-04-01T23:59:59.000Z

242

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

243

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

244

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

245

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

246

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

247

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

248

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

249

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.

250

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

251

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.

252

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

253

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

254

A Privacy-Aware Architecture For Demand Response Systems Stephen Wicker, Robert Thomas  

E-Print Network [OSTI]

A Privacy-Aware Architecture For Demand Response Systems Stephen Wicker, Robert Thomas School architectures that realize the benefits of demand response without requiring that AMI data be centrally-based demand response efforts in the face of public outcry. We also show that Trusted Platform Modules can

Wicker, Stephen

255

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

256

A Hierarchical Demand Response Framework for Data Center Power Cost Optimization Under Real-World Electricity Pricing  

E-Print Network [OSTI]

1 A Hierarchical Demand Response Framework for Data Center Power Cost Optimization Under Real bills. Our focus is on a subset of this work that carries out demand response (DR) by modulating

Urgaonkar, Bhuvan

257

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

258

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

259

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

260

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

262

Assumptions to the Annual Energy Outlook 2001 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions 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, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

263

Macroeconomic Activity Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 19 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook2011 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module.

264

DOE DEMANDS SOLAR PATENTS  

Science Journals Connector (OSTI)

THE DEPARTMENT of Energy is claiming ownership of three patents awarded to Evergreen Solar and plans to prevent them from being sold to non-U.S. ... Even with the innovation, Evergreen—like U.S. solar firms Solyndra and SpectraWatt, which recently both declared bankruptcy—could not compete with lower cost crystalline solar modules made in China. ...

MELODY BOMGARDNER

2011-10-17T23:59:59.000Z

265

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.

266

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.

267

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

268

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

269

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

270

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

271

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

272

New and Underutilized Technology: Carbon Dioxide Demand Ventilation Control  

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

Carbon Dioxide Demand Ventilation Carbon Dioxide Demand Ventilation Control New and Underutilized Technology: Carbon Dioxide Demand Ventilation Control October 4, 2013 - 4:23pm Addthis The following information outlines key deployment considerations for carbon dioxide (CO2) demand ventilation control within the Federal sector. Benefits Demand ventilation control systems modulate ventilation levels based on current building occupancy, saving energy while still maintaining proper indoor air quality (IAQ). CO2 sensors are commonly used, but a multiple-parameter approach using total volatile organic compounds (TVOC), particulate matter (PM), formaldehyde, and relative humidity (RH) levels can also be used. CO2 sensors control the outside air damper to reduce the amount of outside air that needs to be conditioned and supplied to the building when

273

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

274

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

275

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

276

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

277

Demand Charges | Open Energy Information  

Open Energy Info (EERE)

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

278

Liquid Fuels Market Module  

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

Liquid Fuels Market Module Liquid Fuels Market Module This page inTenTionally lefT blank 145 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Liquid Fuels Market Module The NEMS Liquid Fuels Market Module (LFMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, esters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the LFMM projects capacity expansion and fuel consumption at domestic refineries. The LFMM contains a linear programming (LP) representation of U.S. petroleum refining

279

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

280

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

282

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

283

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

284

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

285

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

286

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

287

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

288

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

289

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.

290

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...

291

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...

292

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

293

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

294

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

295

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

296

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

297

NEMS integrating module documentation report  

SciTech Connect (OSTI)

The National Energy Modeling System (NEMS) is a computer-based, energy-economy modeling system of U.S. energy markets for the midterm period. NEMS projects the production, imports, conversion, consumption, and prices of energy, subject to a variety of assumptions. The assumptions encompass macroeconomic and financial factors, world energy markets, resource availability and costs, behavioral and technological choice criteria, technology characteristics, and demographics. NEMS produces a general equilibrium solution for energy supply and demand in the U.S. energy markets on an annual basis through 2015. Baseline forecasts from NEMS are published in the Annual Energy Outlook. Analyses are also prepared in response to requests by the U.S. Congress, the DOE Office of Policy, and others. NEMS was first used for forecasts presented in the Annual Energy Outlook 1994.

NONE

1997-05-01T23:59:59.000Z

298

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

299

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

300

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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.

302

PDSF Modules  

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

Modules Modules Modules Modules Approach to Managing The Environment Modules is a system which you can use to specify what software you want to use. If you want to use a particular software package loading its module will take care of the details of modifying your environment as necessary. The advantage of the modules approach is that the you are not required to explicitly specify paths for different executable versions and try to keep their related man paths and environment variables coordinated. Instead you simply "load" and "unload" specific modules to control your environment. Getting Started with Modules If you're using the standard startup files on PDSF then you're already setup for using modules. If the "module" command is not available, please

303

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

304

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

305

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

306

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

307

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

308

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

309

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

310

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

311

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

312

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

313

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

314

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

315

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

316

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

317

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)

318

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

319

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

320

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

322

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

323

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

324

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

325

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

326

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...

327

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

328

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...

329

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...

330

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

331

Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network [OSTI]

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

332

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

333

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

334

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,...

335

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

336

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

337

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

338

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

339

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

340

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

342

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

343

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

344

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

345

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

346

Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant  

Science Journals Connector (OSTI)

This paper presents an artificial neural network (ANN) approach for forecasting the performance of electric energy generated output from a working 25-kWp grid connected solar PV system and a 100-kWp grid connected PV system installed at Minicoy Island of Union Territory of Lakshadweep Islands. The ANN interpolates among the solar PV generation output and relevant parameters such as solar radiation, module temperature and clearness index. In this study, three ANN models are implemented and validated with reasonable accuracy on real electric energy generation output data. The first model is univariate based on solar radiation and the output values. The second model is a multivariate model based on module temperature along with solar radiation. The third model is also a multivariate model based on module temperature, solar radiation and clearness index. A forecasting performance measure such as percentage root mean square error has been presented for each model. The second model, which gives the most accurate results, has been used in forecasting the generation output for another PV system with similar accuracy.

Imtiaz Ashraf; A. Chandra

2004-01-01T23:59:59.000Z

347

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

348

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

349

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

350

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

351

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

352

timber quality Modelling and forecasting  

E-Print Network [OSTI]

facilities match the more traditional requirements of timber production. As this policy evolves will also incorporate carbon and energy budgeting modules to assist in the cost­benefit analysis of forest aimed at the optimisation of sustainable management, the provision of renewable resources

353

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

354

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

355

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

356

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

357

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

358

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

359

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

360

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

362

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

363

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

364

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

365

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

366

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.

367

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

368

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

369

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

370

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...

371

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

372

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

373

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

374

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

375

NEMS Freight Transportation Module Improvement Study  

Reports and Publications (EIA)

The U.S. Energy Information Administration (EIA) contracted with IHS Global, Inc. (IHS) to analyze the relationship between the value of industrial output, physical output, and freight movement in the United States for use in updating analytic assumptions and modeling structure within the National Energy Modeling System (NEMS) freight transportation module, including forecasting methodologies and processes to identify possible alternative approaches that would improve multi-modal freight flow and fuel consumption estimation.

2015-01-01T23:59:59.000Z

376

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

377

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

378

Module Configuration  

DOE Patents [OSTI]

A stand alone battery module including: (a) a mechanical configuration; (b) a thermal management configuration; (c) an electrical connection configuration; and (d) an electronics configuration. Such a module is fully interchangeable in a battery pack assembly, mechanically, from the thermal management point of view, and electrically. With the same hardware, the module can accommodate different cell sizes and, therefore, can easily have different capacities. The module structure is designed to accommodate the electronics monitoring, protection, and printed wiring assembly boards (PWAs), as well as to allow airflow through the module. A plurality of modules may easily be connected together to form a battery pack. The parts of the module are designed to facilitate their manufacture and assembly.

Oweis, Salah (Ellicott City, MD); D'Ussel, Louis (Bordeaux, FR); Chagnon, Guy (Cockeysville, MD); Zuhowski, Michael (Annapolis, MD); Sack, Tim (Cockeysville, MD); Laucournet, Gaullume (Paris, FR); Jackson, Edward J. (Taneytown, MD)

2002-06-04T23:59:59.000Z

379

Assumptions to the Annual Energy Outlook 2001 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2001, DOE/EIA-M060(2001) January 2001. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

380

Assumptions to the Annual Energy Outlook 2002 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2002, DOE/EIA-M060(2002) (Washington, DC, January 2002). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

Note: This page contains sample records for the topic "demand module forecasts" 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

Assumptions to the Annual Energy Outlook 2000 - Electricity Market Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. The major assumptions are summarized below.

382

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

383

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

384

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

385

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

386

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

387

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

388

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

389

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

390

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

391

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

392

Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 137 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Administration for

393

Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

This page inTenTionally lefT blank 135 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, esters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Administration for

394

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"

395

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

396

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

397

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

398

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

399

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...

400

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 "demand module forecasts" 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

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

402

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

403

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...

404

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...

405

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

406

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

407

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

408

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

409

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

410

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

411

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:

412

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

413

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.

414

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

415

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

416

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

417

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

418

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...

419

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

420

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

Note: This page contains sample records for the topic "demand module forecasts" 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

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

422

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

423

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

424

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

425

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...

426

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

427

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.

428

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

429

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...

430

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

431

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

432

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

433

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

434

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

435

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

436

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

437

Opportunities, Barriers and Actions for Industrial Demand Response in California  

E-Print Network [OSTI]

and Techniques for Demand Response, report for theand Reliability Demand Response Programs: Final Report.Demand Response

McKane, Aimee T.

2009-01-01T23:59:59.000Z

438

Automated Demand Response Opportunities in Wastewater Treatment Facilities  

E-Print Network [OSTI]

Interoperable Automated Demand Response Infrastructure,study of automated demand response in wastewater treatmentopportunities for demand response control strategies in

Thompson, Lisa

2008-01-01T23:59:59.000Z

439

Global energy demand to 2060  

SciTech Connect (OSTI)

The projection of global energy demand to the year 2060 is of particular interest because of its relevance to the current greenhouse concerns. The long-term growth of global energy demand in the time scale of climatic change has received relatively little attention in the public discussion of national policy alternatives. The sociological, political, and economic issues have rarely been mentioned in this context. This study emphasizes that the two major driving forces are global population growth and economic growth (gross national product per capita), as would be expected. The modest annual increases assumed in this study result in a year 2060 annual energy use of >4 times the total global current use (year 1986) if present trends continue, and >2 times with extreme efficiency improvements in energy use. Even assuming a zero per capita growth for energy and economics, the population increase by the year 2060 results in a 1.5 times increase in total annual energy use.

Starr, C. (Electric Power Research Institute, Palo Alto, CA (USA))

1989-01-01T23:59:59.000Z

440

Energy Demand | Open Energy Information  

Open Energy Info (EERE)

Energy Demand Energy Demand Jump to: navigation, search Click to return to AEO2011 page AEO2011 Data Figure 55 From AEO2011 report . Market Trends Growth in energy use is linked to population growth through increases in housing, commercial floorspace, transportation, and goods and services. These changes affect not only the level of energy use, but also the mix of fuels used. Energy consumption per capita declined from 337 million Btu in 2007 to 308 million Btu in 2009, the lowest level since 1967. In the AEO2011 Reference case, energy use per capita increases slightly through 2013, as the economy recovers from the 2008-2009 economic downturn. After 2013, energy use per capita declines by 0.3 percent per year on average, to 293 million Btu in 2035, as higher efficiency standards for vehicles and

Note: This page contains sample records for the topic "demand module forecasts" 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

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect (OSTI)

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02T23:59:59.000Z

442

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

443

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

444

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network [OSTI]

Forecasting the Locational Dynamics of Transnational Terrorism: A Network Analytic Approach Bruce A-0406 Fax: (919) 962-0432 Email: skyler@unc.edu Abstract--Efforts to combat and prevent transnational terror of terrorism. We construct the network of transnational terrorist attacks, in which source (sender) and target

Massachusetts at Amherst, University of

445

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

' · Empirical models quantify our ability to predict without knowing the laws of physics · Climatology skill' model? 2. Dynamic climatology (DC) is a more appropriate benchmark for near- term (initialised) climate forecasts · A conditional climatology, initialised at launch and built from the historical archive

Stevenson, Paul

446

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

... For decades, companies have tailored public weather data for private customers from farmers to airlines. On Wednesday, a group of businesses said that they ... utilities and satellite operators. But Terry Onsager, a physicist at the SWPC, says that private forecasting firms are starting to realize that they can add value to these predictions. ...

Eric Hand

2012-04-27T23:59:59.000Z

447

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

448

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

Prediction versus Projection: How weather forecasting and climate models differ. Aaron B. Wilson Context: Global http://data.giss.nasa.gov/ #12;Numerical Weather Prediction Collect Observations alters associated weather patterns. Models used to predict weather depend on the current observed state

Howat, Ian M.

449

Customized forecasting tool improves reserves estimation  

SciTech Connect (OSTI)

Unique producing characteristics of the Teapot sandstone formation, Powder River basin, Wyoming, necessitated the creation of individualized production forecasting methods for wells producing from this reservoir. The development and use of a set of production type curves and correlations for Teapot wells are described herein.

Mian, M.A.

1986-04-01T23:59:59.000Z

450

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

...But the WRF has no immediate...being tuned to local conditions...temperatures and winds with altitude...resulting WRF forecasts...captured the local sea-breeze winds better...spread the local operation of mesoscale...to be the WRF model now...

Richard A. Kerr

2004-05-14T23:59:59.000Z

451

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

#12;FORECAST OF VACANCIES Until end of 2016 (Issue No. 22) #12;Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff the team of professionals. Second half 2015 VACANCY GRADE REQUIREMENTS / ROLE EXPECTED DATE OF VACANCY

452

Online short-term solar power forecasting  

SciTech Connect (OSTI)

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)

Bacher, Peder; Madsen, Henrik [Informatics and Mathematical Modelling, Richard Pedersens Plads, Technical University of Denmark, Building 321, DK-2800 Lyngby (Denmark); Nielsen, Henrik Aalborg [ENFOR A/S, Lyngsoe Alle 3, DK-2970 Hoersholm (Denmark)

2009-10-15T23:59:59.000Z

453

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

454

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

455

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

456

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

ANL Wind Power Forecasting and Electricity Markets ANL Wind Power Forecasting and Electricity Markets Jump to: navigation, search Logo: Wind Power Forecasting and Electricity Markets Name Wind Power Forecasting and Electricity Markets Agency/Company /Organization Argonne National Laboratory Partner Institute for Systems and Computer Engineering of Porto (INESC Porto) in Portugal, Midwest Independent System Operator and Horizon Wind Energy LLC, funded by U.S. Department of Energy Sector Energy Focus Area Wind Topics Pathways analysis, Technology characterizations Resource Type Software/modeling tools Website http://www.dis.anl.gov/project References Argonne National Laboratory: Wind Power Forecasting and Electricity Markets[1] Abstract To improve wind power forecasting and its use in power system and electricity market operations Argonne National Laboratory has assembled a team of experts in wind power forecasting, electricity market modeling, wind farm development, and power system operations.

457

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

458

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

to the to the Annual Energy Outlook 1998 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Household Expenditures Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Oil and Gas Supply Module

459

Hawaii demand-side management resource assessment. Final report: DSM opportunity report  

SciTech Connect (OSTI)

The Hawaii Demand-Side Management Resource Assessment was the fourth of seven projects in the Hawaii Energy Strategy (HES) program. HES was designed by the Department of Business, Economic Development, and Tourism (DBEDT) to produce an integrated energy strategy for the State of Hawaii. The purpose of Project 4 was to develop a comprehensive assessment of Hawaii`s demand-side management (DSM) resources. To meet this objective, the project was divided into two phases. The first phase included development of a DSM technology database and the identification of Hawaii commercial building characteristics through on-site audits. These Phase 1 products were then used in Phase 2 to identify expected energy impacts from DSM measures in typical residential and commercial buildings in Hawaii. The building energy simulation model DOE-2.1E was utilized to identify the DSM energy impacts. More detailed information on the typical buildings and the DOE-2.1E modeling effort is available in Reference Volume 1, ``Building Prototype Analysis``. In addition to the DOE-2.1E analysis, estimates of residential and commercial sector gas and electric DSM potential for the four counties of Honolulu, Hawaii, Maui, and Kauai through 2014 were forecasted by the new DBEDT DSM Assessment Model. Results from DBEDTs energy forecasting model, ENERGY 2020, were linked with results from DOE-2.1E building energy simulation runs and estimates of DSM measure impacts, costs, lifetime, and anticipated market penetration rates in the DBEDT DSM Model. Through its algorithms, estimates of DSM potential for each forecast year were developed. Using the load shape information from the DOE-2.1E simulation runs, estimates of electric peak demand impacts were developed. 10 figs., 55 tabs.

NONE

1995-08-01T23:59:59.000Z

460

Demand Side Bidding. Final Report  

SciTech Connect (OSTI)

This document sets forth the final report for a financial assistance award for the National Association of Regulatory Utility Commissioners (NARUC) to enhance coordination between the building operators and power system operators in terms of demand-side responses to Location Based Marginal Pricing (LBMP). Potential benefits of this project include improved power system reliability, enhanced environmental quality, mitigation of high locational prices within congested areas, and the reduction of market barriers for demand-side market participants. NARUC, led by its Committee on Energy Resources and the Environment (ERE), actively works to promote the development and use of energy efficiency and clean distributive energy policies within the framework of a dynamic regulatory environment. Electric industry restructuring, energy shortages in California, and energy market transformation intensifies the need for reliable information and strategies regarding electric reliability policy and practice. NARUC promotes clean distributive generation and increased energy efficiency in the context of the energy sector restructuring process. NARUC, through ERE's Subcommittee on Energy Efficiency, strives to improve energy efficiency by creating working markets. Market transformation seeks opportunities where small amounts of investment can create sustainable markets for more efficient products, services, and design practices.

Spahn, Andrew

2003-12-31T23:59:59.000Z

Note: This page contains sample records for the topic "demand module forecasts" 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

Definition: Peak Demand | Open Energy Information  

Open Energy Info (EERE)

Peak Demand Peak Demand Jump to: navigation, search Dictionary.png Peak Demand The highest hourly integrated Net Energy For Load within a Balancing Authority Area occurring within a given period (e.g., day, month, season, or year)., The highest instantaneous demand within the Balancing Authority Area.[1] View on Wikipedia Wikipedia Definition Peak demand is used to refer to a historically high point in the sales record of a particular product. In terms of energy use, peak demand describes a period of strong consumer demand. Related Terms Balancing Authority Area, energy, demand, balancing authority, smart grid References ↑ Glossary of Terms Used in Reliability Standards An inli LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ne Glossary Definition Retrieved from

462

Demand Response Programs Oregon Public Utility Commission  

E-Print Network [OSTI]

Demand Response Programs Oregon Public Utility Commission January 6, 2005 Mike Koszalka Director;Demand Response Results, 2004 Load Control ­ Cool Keeper ­ ID Irrigation Load Control Price Responsive

463

Industrial Equipment Demand and Duty Factors  

E-Print Network [OSTI]

Demand and duty factors have been measured for selected equipment (air compressors, electric furnaces, injection molding machines, centrifugal loads, and others) in industrial plants. Demand factors for heavily loaded air compressors were near 100...

Dooley, E. S.; Heffington, W. M.

464

ConservationandDemand ManagementPlan  

E-Print Network [OSTI]

; Introduction Ontario Regulation 397/11 under the Green Energy Act 2009 requires public agencies and implement energy Conservation and Demand Management (CDM) plans starting in 2014. Requirementsofthe ConservationandDemand ManagementPlan 2014-2019 #12

Abolmaesumi, Purang

465

Energy Demand Analysis at a Disaggregated Level  

Science Journals Connector (OSTI)

The purpose of this chapter is to consider energy demand at the fuel level or at the ... . This chapter first presents the disaggregation of energy demand, discusses the information issues and introduces framewor...

Subhes C. Bhattacharyya

2011-01-01T23:59:59.000Z

466

Seasonal temperature variations and energy demand  

Science Journals Connector (OSTI)

This paper presents an empirical study of the relationship between residential energy demand and temperature. Unlike previous studies in this ... different regions and to the contrasting effects on energy demand ...

Enrica De Cian; Elisa Lanzi; Roberto Roson

2013-02-01T23:59:59.000Z

467

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

4 4 Notes: This graph shows monthly average spot West Texas Intermediate crude oil prices. Spot WTI crude oil prices peaked last fall as anticipated boosts to world supply from OPEC and other sources did not show up in actual stocks data. So where do we see crude oil prices going from here? Crude oil prices are expected to be about $28-$30 per barrel for the rest of this year, but note the uncertainty bands on this projection. They give an indication of how difficult it is to know what these prices are going to do. Also, EIA does not forecast volatility. This relatively flat forecast could be correct on average, with wide swings around the base line. Let's explore why we think prices will likely remain high, by looking at an important market barometer - inventories - which measures the

468

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

FRED FRED http://en.openei.org/community/group/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. energy data + forecasting Fri, 22 Jun 2012 15:30:20 +0000 Dbrodt 34

469

Voluntary Green Power Market Forecast through 2015  

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

158 158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 * www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Operated by the Alliance for Sustainable Energy, LLC Contract No. DE-AC36-08-GO28308 Technical Report NREL/TP-6A2-48158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory

470

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Highlights Highlights World energy consumption is projected to increase by 57 percent from 2002 to 2025. Much of the growth in worldwide energy use in the IEO2005 reference case forecast is expected in the countries with emerging economies. Figure 1. World Marketed Energy Consumptiion by Region, 1970-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data In the International Energy Outlook 2005 (IEO2005) reference case, world marketed energy consumption is projected to increase on average by 2.0 percent per year over the 23-year forecast horizon from 2002 to 2025—slightly lower than the 2.2-percent average annual growth rate from 1970 to 2002. Worldwide, total energy use is projected to grow from 412 quadrillion British thermal units (Btu) in 2002 to 553 quadrillion Btu in

471

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

472

Decentralized demand management for water distribution  

E-Print Network [OSTI]

. Actual Daily Demand for Model 2 . . 26 4 Predicted vs. Actual Peak Hourly Demand for Model 1 27 5 Predicted vs. Actual Peak Hourly Demand for Model 2 28 6 Cumulative Hourly Demand Distribution 7 Bryan Distribution Network 8 Typical Summer Diurnal... locating and controlling water that has not been accounted for. The Ford Meter Box Company (1987) advises the testing and recalibration of existing water meters. Because operating costs in a distribution network can be quite substantial, a significant...

Zabolio, Dow Joseph

2012-06-07T23:59:59.000Z

473

Exponential smoothing models: Means and variances for lead-time demand  

Science Journals Connector (OSTI)

Exponential smoothing is often used to forecast lead-time demand (LTD) for inventory control. In this paper, formulae are provided for calculating means and variances of LTD for a wide variety of exponential smoothing methods. A feature of many of the formulae is that variances, as well as the means, depend on trends and seasonal effects. Thus, these formulae provide the opportunity to implement methods that ensure that safety stocks adjust to changes in trend or changes in season. An example using weekly sales shows how safety stocks can be seriously underestimated during peak sales periods.

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

2004-01-01T23:59:59.000Z

474

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30T23:59:59.000Z

475

Demand Responsive Lighting: A Scoping Study  

E-Print Network [OSTI]

LBNL-62226 Demand Responsive Lighting: A Scoping Study F. Rubinstein, S. Kiliccote Energy Environmental Technologies Division January 2007 #12;LBNL-62226 Demand Responsive Lighting: A Scoping Study in this report was coordinated by the Demand Response Research Center and funded by the California Energy

476

Demand Response Resources in Pacific Northwest  

E-Print Network [OSTI]

Demand Response Resources in Pacific Northwest Chuck Goldman Lawrence Berkeley National Laboratory cagoldman@lbl.gov Pacific Northwest Demand Response Project Portland OR May 2, 2007 #12;Overview · Typology Annual Reports ­ Journal articles/Technical reports #12;Demand Response Resources · Incentive

477

Leveraging gamification in demand dispatch systems  

Science Journals Connector (OSTI)

Modern demand-side management techniques are an integral part of the envisioned smart grid paradigm. They require an active involvement of the consumer for an optimization of the grid's efficiency and a better utilization of renewable energy sources. ... Keywords: demand response, demand side management, direct load control, gamification, smart grid, sustainability

Benjamin Gnauk; Lars Dannecker; Martin Hahmann

2012-03-01T23:59:59.000Z

478

Demand Response and Ancillary Services September 2008  

E-Print Network [OSTI]

Demand Response and Ancillary Services September 2008 #12;© 2008 EnerNOC, Inc. All Rights Reserved programs The purpose of this presentation is to offer insight into the mechanics of demand response and industrial demand response resources across North America in both regulated and restructured markets As of 6

479

THE STATE OF DEMAND RESPONSE IN CALIFORNIA  

E-Print Network [OSTI]

THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response can help reduce the threat of planned rotational outages. Demand response is also widely regarded as having

480

THE STATE OF DEMAND RESPONSE IN CALIFORNIA  

E-Print Network [OSTI]

THE STATE OF DEMAND RESPONSE IN CALIFORNIA Prepared For: California Energy in this report. #12; ABSTRACT By reducing system loads during criticalpeak times, demand response (DR) can.S. and internationally and lay out ideas that could help move California forward. KEY WORDS demand response, peak

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to obtain the most current and comprehensive results.


481

Modeling Energy Demand Aggregators for Residential Consumers  

E-Print Network [OSTI]

The current world-wide increase of energy demand cannot be matched by energy production and power grid updateModeling Energy Demand Aggregators for Residential Consumers G. Di Bella, L. Giarr`e, M. Ippolito, A. Jean-Marie, G. Neglia and I. Tinnirello § January 2, 2014 Abstract Energy demand aggregators

Paris-Sud XI, Université de

482

Response to changes in demand/supply  

E-Print Network [OSTI]

Response to changes in demand/supply through improved marketing 21.2 #12;#12;111 Impacts of changes log demand in 1995. The composites board mills operating in Korea took advantage of flexibility environment changes on the production mix, some economic indications, statistics of demand and supply of wood

483

Response to changes in demand/supply  

E-Print Network [OSTI]

Response to changes in demand/supply through improved marketing 21.2 http with the mill consuming 450 000 m3 , amounting to 30% of total plywood log demand in 1995. The composites board, statistics of demand and supply of wood, costs and competitiveness were analysed. The reactions

484

Smart Buildings Using Demand Response March 6, 2011  

E-Print Network [OSTI]

Smart Buildings Using Demand Response March 6, 2011 Sila Kiliccote Deputy, Demand Response Division Lawrence Berkeley National Laboratory Demand Response Research Center 1 #12;Presentation Outline Demand Response Research Center ­ DRRC Vision and Research Portfolio Introduction to Demand

Kammen, Daniel M.

485

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approaches provide the best basis for automated method selection, the Akaike information criteria having a slight edge over its information criteria counterparts.

Baki Billah; Maxwell L. King; Ralph D. Snyder; Anne B. Koehler

2006-01-01T23:59:59.000Z

486

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the method presented here can be used to forecast solar wind velocities and magnetic polarity from a single coronal hole image, along with a single magnetic full-disk image. The coronal hole parameters used in this study are estimated with Kitt Peak Vacuum Telescope He I 1083 nm spectrograms and photospheric magnetograms. Solar wind and coronal hole data for the period between May 1992 and September 2003 are investigated. The new model is found to be accurate to within 10% of observed solar wind measurements for its best one-month periods, and it has a linear correlation coefficient of ~0.38 for the full 11 years studied. Using a single estimated coronal hole map, the model can forecast the Earth directed solar wind velocity up to 8.5 days in advance. In addition, this method can be used with any source of coronal hole area and location data.

S. Robbins; C. J. Henney; J. W. Harvey

2007-01-09T23:59:59.000Z

487

Assumptions to the Annual Energy Outlook 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

488

Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

6, DOE/EIA- 6, DOE/EIA- M068(2006). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described. EMM Regions The supply regions used in EMM are based on the North American Electric Reliability Council regions and

489

Energy demand and population changes  

SciTech Connect (OSTI)

Since World War II, US energy demand has grown more rapidly than population, so that per capita consumption of energy was about 60% higher in 1978 than in 1947. Population growth and the expansion of per capita real incomes have led to a greater use of energy. The aging of the US population is expected to increase per capita energy consumption, despite the increase in the proportion of persons over 65, who consume less energy than employed persons. The sharp decline in the population under 18 has led to an expansion in the relative proportion of population in the prime-labor-force age groups. Employed persons are heavy users of energy. The growth of the work force and GNP is largely attributable to the growing participation of females. Another important consequence of female employment is the growth in ownership of personal automobiles. A third factor pushing up labor-force growth is the steady influx of illegal aliens.

Allen, E.L.; Edmonds, J.A.

1980-12-01T23:59:59.000Z

490

Effects of the Financial Crisis on Photovoltaics: An Analysis of Changes in Market Forecasts from 2008 to 2009  

SciTech Connect (OSTI)

To examine how the financial crisis has impacted expectations of photovoltaic production, demand and pricing over the next several years, we surveyed the market forecasts of industry analysts that had issued projections in 2008 and 2009. We find that the financial crisis has had a significant impact on the PV industry, primarily through increasing the cost and reducing the availability of investment into the sector. These effects have been more immediately experienced by PV installations than by production facilities, due to the different types and duration of investments, and thus PV demand has been reduced by a greater proportion than PV production. By reducing demand more than production, the financial crisis has accelerated previously expected PV overcapacity and resulting price declines.

Bartlett, J. E.; Margolis, R. M.; Jennings, C. E.

2009-09-01T23:59:59.000Z

491

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).

492

Electricity demand analysis - unconstrained vs constrained scenarios  

Science Journals Connector (OSTI)

In India, the electricity systems are chronically constrained by shortage of both capital and energy resources. These result in rationing and interruptions of supply with a severely disrupted electricity usage pattern. From this background, we try to analyse the demand patterns with and without resource constraints. Accordingly, it is necessary to model appropriately the dynamic nature of electricity demand, which cannot be captured by methods like annual load duration curves. Therefore, we use the concept - Representative Load Curves (RLCs) - to model the temporal and structural variations in demand. As a case study, the electricity system of the state of Karnataka in India is used. Four years demand data, two unconstrained and two constrained, are used and RLCs are developed using multiple discriminant analysis. It is found that these RLCs adequately model the variations in demand and bring out distinctions between unconstrained and constrained demand patterns. The demand analysis attempted here helped to study the differences in demand patterns with and without constraints, and the success of rationing measures in reducing demand levels as well as greatly disrupting the electricity usage patterns. Multifactor ANOVA analyses are performed to find out the statistical significance of the ability of logically obtained factors in explaining overall variations in demand. The results showed that the factors that are taken into consideration accounted for maximum variations in demand at very high significance levels.

P. Balachandra; V. Chandru; M.H. Bala Subrahmanya

2003-01-01T23:59:59.000Z

493

Today's Forecast: Improved Wind Predictions | Department of Energy  

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

494

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2004 with Projections to 2025 Forecast Comparisons Index (click to jump links) Economic Growth World Oil Prices Total Energy Consumption Electricity Natural Gas Petroleum Coal The AEO2004 forecast period extends through 2025. One other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a similar time horizon. Several others provide forecasts that address one or more aspects of energy markets over different time horizons. Recent projections from GII and others are compared here with the AEO2004 projections. Economic Growth Printer Friendly Version Average annual percentage growth Forecast 2002-2008 2002-2013 2002-2025 AEO2003 3.2 3.3 3.1 AEO2004 Reference 3.3 3.2 3.0

495

Today's Forecast: Improved Wind Predictions | Department of Energy  

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

496

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

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

2013-10-01T23:59:59.000Z

497

Measurement and Verification for Demand Response  

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

Measurement and Verification for Measurement and Verification for Demand Response Prepared for the National Forum on the National Action Plan on Demand Response: Measurement and Verification Working Group AUTHORS: Miriam L. Goldberg & G. Kennedy Agnew-DNV KEMA Energy and Sustainability National Forum of the National Action Plan on Demand Response Measurement and Verification for Demand Response was developed to fulfill part of the Implementation Proposal for The National Action Plan on Demand Response, a report to Congress jointly issued by the U.S. Department of Energy (DOE) and the Federal Energy Regulatory Commission (FERC) in June 2011. Part of that implementation proposal called for a "National Forum" on demand response to be conducted by DOE and FERC. Given that demand response has matured, DOE and FERC decided that a "virtual" project

498

Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes  

E-Print Network [OSTI]

Secure Demand Shaping for Smart Grid On constructing probabilistic demand response schemes. Developing novel schemes for demand response in smart electric gird is an increasingly active research area/SCADA for demand response in smart infrastructures face the following dilemma: On one hand, in order to increase

Sastry, S. Shankar

499

US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier  

E-Print Network [OSTI]

that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controllingUS Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach Massimo www.cepe.ethz.ch #12;US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier

500

Assumptions to the Annual Energy Outlook 2000 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module estimates energy consumption across the nine Census Divisions 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, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Transportation Demand Module estimates energy consumption across the nine Census Divisions 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, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs