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1

Time Series Prediction Forecasting the Future and ...  

Science Conference Proceedings (OSTI)

Time Series Prediction Forecasting the Future and Understanding the Past Santa Fe Institute Proceedings on the Studies in the Sciences of ...

2012-10-01T23:59:59.000Z

2

Forecasting future volatility from option prices, Working  

E-Print Network (OSTI)

Weisbach are gratefully acknowledged. I bear full responsibility for all remaining errors. Forecasting Future Volatility from Option Prices Evidence exists that option prices produce biased forecasts of future volatility across a wide variety of options markets. This paper presents two main results. First, approximately half of the forecasting bias in the S&P 500 index (SPX) options market is eliminated by constructing measures of realized volatility from five minute observations on SPX futures rather than from daily closing SPX levels. Second, much of the remaining forecasting bias is eliminated by employing an option pricing model that permits a non-zero market price of volatility risk. It is widely believed that option prices provide the best forecasts of the future volatility of the assets which underlie them. One reason for this belief is that option prices have the ability to impound all publicly available information – including all information contained in the history of past prices – about the future volatility of the underlying assets. A second related reason is that option pricing theory maintains that if an option prices fails to embody optimal forecasts of the future volatility of the underlying asset, a profitable trading strategy should be available whose implementation would push the option price to the level that reflects the best possible forecast of future volatility.

Allen M. Poteshman

2000-01-01T23:59:59.000Z

3

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network (OSTI)

Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery orders using forecasts provided by their customers. Our goal is to improve the supplier's operations through a better un- derstanding of the customers's forecast behavior. Unfortunately, customer forecasts

Murphy, Robert F.

4

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

E-Print Network (OSTI)

Eighth Electric Utility Forecasting Symposium in Baltimore,Development of a Residential Forecasting Database. Lawrenceand Methodology for End-Use Forecasting with EPRI-REEPS 2.1.

Johnson, F.X.

2010-01-01T23:59:59.000Z

5

LBL-34044 UC-1600 RESIDENTIAL SECTOR END-USE FORECASTING WITH...  

NLE Websites -- All DOE Office Websites (Extended Search)

primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast...

6

Consumer life-cycle cost impacts of energy-efficiency standards for residential-type central air conditioners and heat pumps  

E-Print Network (OSTI)

was used to forecast electricity prices into the future (Case forecasts residential electricity prices to decline to

Rosenquist, Gregory; Chan, Peter; Lekov, Alex; McMahon, James; Van Buskirk, Robert

2001-01-01T23:59:59.000Z

7

Baseline data for the residential sector and development of a residential forecasting database  

SciTech Connect

This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

1994-05-01T23:59:59.000Z

8

Optimal Updating of Forecasts for the Timing of Future Events  

Science Conference Proceedings (OSTI)

A major problem in forecasting is estimating the time of some future event. traditionally, forecasts are designed to minimize an error cost function that is evaluated once, possibly when the event occurs and forecast accuracy can be determined. However, ... Keywords: Air Transportation, Dynamic Programming Applications, Forecasting

Juhwen Hwang; Medini R. Singh; W. J. Hurley; Robert A. Shumsky

1998-03-01T23:59:59.000Z

9

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

Development of a Residential Forecasting Database. Lawrenceand Methodology for End-Use Forecasting with EPRI-REEPS 2.1.and Methodology for End-Use Forecasting with EPRI-REEPS 2.1.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

10

Mathematical and computer modelling reports: Modeling and forecasting energy markets with the intermediate future forecasting system  

Science Conference Proceedings (OSTI)

This paper describes the Intermediate Future Forecasting System (IFFS), which is the model used to forecast integrated energy markets by the U.S. Energy Information Administration. The model contains representations of supply and demand for all of the ...

Frederic H. Murphy; John J. Conti; Susan H. Shaw; Reginald Sanders

1989-09-01T23:59:59.000Z

11

Operational Forecaster Uncertainty Needs and Future Roles  

Science Conference Proceedings (OSTI)

Key results of a comprehensive survey of U.S. National Weather Service operational forecast managers concerning the assessment and communication of forecast uncertainty are presented and discussed. The survey results revealed that forecasters are ...

David R. Novak; David R. Bright; Michael J. Brennan

2008-12-01T23:59:59.000Z

12

Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?  

E-Print Network (OSTI)

with Residential Electricity Demand in India's Future - How2008). The Boom of Electricity Demand in the residential2005). Forecasting Electricity Demand in Developing

Letschert, Virginie

2010-01-01T23:59:59.000Z

13

Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1  

Science Conference Proceedings (OSTI)

This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

1994-05-01T23:59:59.000Z

14

ANN-based residential water end-use demand forecasting model  

Science Conference Proceedings (OSTI)

Bottom-up urban water demand forecasting based on empirical data for individual water end uses or micro-components (e.g., toilet, shower, etc.) for different households of varying characteristics is undoubtedly superior to top-down estimates originating ... Keywords: Artificial neural network, Residential water demand forecasting, Water demand management, Water end use, Water micro-component

Christopher Bennett; Rodney A. Stewart; Cara D. Beal

2013-03-01T23:59:59.000Z

15

Expert Panel: Forecast Future Demand for Medical Isotopes  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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

16

Solar future: 1978. [Market forecast to 1992  

SciTech Connect

The growth in sales of solar heating equipment is discussed. Some forecasts are made for the continued market growth of collectors, pool systems, and photovoltaics. (MOW)

Butt, S.H.

1978-03-01T23:59:59.000Z

17

Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results  

SciTech Connect

This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

Koomey, J.G.; Brown, R.E.; Richey, R. [and others

1995-12-01T23:59:59.000Z

18

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

forecasts (or any other forecast, for that matter) in makingcase natural gas price forecast, but to also examine a wideAEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

19

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

NLE Websites -- All DOE Office Websites (Extended Search)

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

20

Fuzzy rule-based methodology for residential load behaviour forecasting during power systems restoration  

Science Conference Proceedings (OSTI)

Inadequate load pickup during power system restoration can lead to overload and underfrequency conditions, and even restart the blackout process, due to thermal energy losses. Thus, load behaviour estimation during restoration is desirable to avoid inadequate ... Keywords: artificial intelligence, energy management systems, fuzzy logic, load behaviour estimation, power system distribution, power system restoration, residential load forecasting, thermostatically controlled loads

Lia Toledo Moreira Mota; Alexandre Assis Mota; Andre Luiz Morelato Franca

2005-04-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

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

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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

22

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

AEO 2009 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series toAEO reference-case gas price forecast compares to the NYMEX

Bolinger, Mark

2009-01-01T23:59:59.000Z

23

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

longer-term market-based forecasts that can be used to more-AEO 2008 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series to

Bolinger, Mark

2008-01-01T23:59:59.000Z

24

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

a portion of the gas price forecast – through 2010 – can beAEO 2006 reference case forecast to conduct a 25-yearAEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

25

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

9: Two Alternative Price Forecasts (denoted by open circlesAEO 2007 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series to

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

26

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

Market and STEO Error Forecast Error from 1998 to 2003 (2 Futures Market and STEO Error Forecast Error from 1998to 2003 (Months 13- Forecast from 1998 to 2003 (Months 1-12)

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

27

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

E-Print Network (OSTI)

to the EIA’s natural gas price forecasts in AEO 2004 and AEOon the AEO 2005 natural gas price forecasts will likely onceof AEO 2005 Natural Gas Price Forecast to NYMEX Futures

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

28

September 2000Forecasting Future Variance from Option Prices  

E-Print Network (OSTI)

Although it is widely believed that option prices provide the best possible forecasts of the future variance of the assets which underlie them, a large body of empirical evidence concludes that option prices consistently yield biased forecasts of future variance. The prevailing interpretation of these findings is that option investors may be forming unbiased forecasts of the future variance of underlying assets but that these unbiased forecasts fail to get impounded into option prices because of either (1) the difficulty of carrying out the necessary arbitrage strategies that would force the prices to their proper levels, or (2) the availability to market makers of lucrative alternative strategies in which they simply profit from the large bid-ask spreads in the options markets. This interpretation has significant consequences for nearly the entire range of option pricing research, since it implies that non-continuous trading, bid-ask spreads, and other market imperfections substantially influence option prices. This implication is important, both because incorporating these types of market imperfections into option pricing models is much more difficult than, for example, altering the dynamics of the underlying asset and also because it suggests that researchers cannot learn about option investor expectations by filtering option

Allen M. Poteshman; Mark R. Manfredo; Allen M. Poteshman; Allen M. Poteshman; Champaign Helpful; Jegadeesh Narasimhan

2000-01-01T23:59:59.000Z

29

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

30

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

E-Print Network (OSTI)

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting. DE-AC03-76SF00098 #12;i ABSTRACT This report details the data, assumptions and methodology for end-use provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

31

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

SciTech Connect

China's rapid economic expansion has propelled it into the ranks of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. Even though the rapid growth is largely attributable to heavy industry, this in turn is driven by rapid urbanization process, by construction materials and equipment produced for use in buildings. Residential energy is mostly used in urban areas, where rising incomes have allowed acquisition of home appliances, as well as increased use of heating in southern China. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modeling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities.

Zhou, Nan; Nishida, Masaru; Gao, Weijun

2008-12-01T23:59:59.000Z

32

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

SciTech Connect

China's rapid economic expansion has propelled it into the ranks of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. Even though the rapid growth is largely attributable to heavy industry, this in turn is driven by rapid urbanization process, by construction materials and equipment produced for use in buildings. Residential energy is mostly used in urban areas, where rising incomes have allowed acquisition of home appliances, as well as increased use of heating in southern China. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modeling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities.

Zhou, Nan; Nishida, Masaru; Gao, Weijun

2008-12-01T23:59:59.000Z

33

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 ..............................................................................3 Residential Forecast Comparison ..............................................................................................5 Nonresidential Forecast Comparisons

34

Assessment of the possibility of forecasting future natural gas curtailments  

Science Conference Proceedings (OSTI)

This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

Lemont, S.

1980-01-01T23:59:59.000Z

35

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

late January 2008, extend its natural gas futures strip anComparison of AEO 2008 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

Bolinger, Mark

2008-01-01T23:59:59.000Z

36

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Figure 9: Two Alternative Price Forecasts (denoted by openComparison of AEO 2007 Natural Gas Price Forecast toNYMEX Futures Prices Date: December 6, 2006 Introduction On

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

37

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

E-Print Network (OSTI)

AEO 2005 reference case oil price forecast and NYMEX oi lthan the reference case oil price forecast for that year. Inoil futures case” where oil prices are based on the NYMEX

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

38

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

this “hybrid” NYMEX-EIA gas price projection still does notonly a portion of the gas price forecast – through 2010 –of AEO 2006 Natural Gas Price Forecast to NYMEX Futures

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

39

Forecasting the Bayes factor of a future observation  

E-Print Network (OSTI)

I present a new procedure to forecast the Bayes factor of a future observation by computing the Predictive Posterior Odds Distribution (PPOD). This can assess the power of future experiments to answer model selection questions and the probability of the outcome, and can be helpful in the context of experiment design. As an illustration, I consider a central quantity for our understanding of the cosmological concordance model, namely the scalar spectral index of primordial perturbations, n_S. I show that the Planck satellite has over 90% probability of gathering strong evidence against n_S = 1, thus conclusively disproving a scale-invariant spectrum. This result is robust with respect to a wide range of choices for the prior on n_S.

Roberto Trotta

2007-03-05T23:59:59.000Z

40

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

range of different plausible price projections, using eitherthat renewables can provide price certainty over even longerof AEO 2009 Natural Gas Price Forecast to NYMEX Futures

Bolinger, Mark

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

against the risk of energy price fluctuations. In theory,The poor track record of energy price forecasting models hasof information about future energy prices, including most

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

42

An evaluation of market penetration forecasting methodologies for new residential and commercial energy technologies  

SciTech Connect

Forecasting market penetration is an essential step in the development and assessment of new technologies. This report reviews several methodologies that are available for market penetration forecasting. The primary objective of this report is to help entrepreneurs understand these methodologies and aid in the selection of one or more of them for application to a particular new technology. This report also illustrates the application of these methodologies, using examples of new technologies, such as the heat pump, drawn from the residential and commercial sector. The report concludes with a brief discussion of some considerations in selecting a forecasting methodology for a particular situation. It must be emphasized that the objective of this report is not to construct a specific market penetration model for new technologies but only to provide a comparative evaluation of methodologies that would be useful to an entrepreneur who is unfamiliar with the range of techniques available. The specific methodologies considered in this report are as follows: subjective estimation methods, market surveys, historical analogy models, time series models, econometric models, diffusion models, economic cost models, and discrete choice models. In addition to these individual methodologies, which range from the very simple to the very complex, two combination approaches are also briefly discussed: (1) the economic cost model combined with the diffusion model and (2) the discrete choice model combined with the diffusion model. This discussion of combination methodologies is not meant to be exhaustive. Rather, it is intended merely to show that many methodologies often can complement each other. A combination of two or more different approaches may be better than a single methodology alone.

Raju, P.S.; Teotia, A.P.S.

1985-05-01T23:59:59.000Z

43

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

SciTech Connect

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

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

1993-12-01T23:59:59.000Z

44

Energy forecasting: the troubled past of looking the future  

SciTech Connect

Energy forecasts have hardly been distinguished by their accuracy. Why forecasts go awry, and the impact these prominent tools have, is explored. A brief review of the record is given. Because of their allure, their popularity in he media, and their usefulness as tools in political battles, forecasts have played a significant role so far. The danger is that they represent and enhance a fix 'em up, tinkering approach, to the detriment of more efficient free-market policies.

Kutler, E.

1986-01-01T23:59:59.000Z

45

Future world oil prices: modeling methodologies and summary of recent forecasts  

SciTech Connect

This paper has three main objectives. First, the various methodologies that have been developed to explain historical oil price changes and forecast future price trends are reviewed and summarized. Second, the paper summarizes recent world oil price forecasts, and, then possible, discusses the methodologies used in formulating those forecasts. Third, utilizing conclusions from the reviews of the modeling methodologies and the recent price forecasts, in combination with an assessment of recent and projected oil market trends, oil price projections are given for the time period 1987 to 2022. The paper argues that modeling methodologies have undergone significant evolution during the past decade as modelers increasingly recognize the complex and constantly changing structure of the world oil market. Unfortunately, at this point in time a consensus about the appropriate methodology to use in formulating oil price forecasts is yet to be reached. There is, however, a general movement toward the opinion that both economic and political factors should be considered when making price projections. Likewise, there is no consensus about future oil price trends. Forecasts differ widely. However, in general, forecasts have been adjusted downwardly in recent years. Further, an overall assessment of the forecasts and recent oil market trends suggests that oil prices will remain constant in real terms for the remainder of the 1980s. Real oil prices are expected to increase by between 2 and 3% during the 1990s and beyond. Forecasters are quick to point out, however, that all forecasts are subject to significant uncertainty. 69 references, 3 figures, 10 tables.

Curlee, T.R.

1985-04-01T23:59:59.000Z

46

A Hybrid ARCH-M and BP Neural Network Model For GSCI Futures Price Forecasting  

Science Conference Proceedings (OSTI)

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast ... Keywords: ANN, ARCH-M, Commodity Index, Forecasting, GSCI

Wen Bo; Wang Shouyang; K. K. Lai

2007-05-01T23:59:59.000Z

47

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

index.html. Appendix A.1 Natural Gas Price Data for FuturesError STEO Error A.1 Natural Gas Price Data for Futuresof forecasts for natural gas prices as reported by the

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

48

On The MSC Forecasters Forums and the Future Role of the Human Forecaster  

Science Conference Proceedings (OSTI)

The Meteorological Service of Canada held a series of three Forecasters Forum meetings between 2003 and 2005 to seek input from the meteorological community on the best ways to implement a restructuring strategy and to develop a common vision ...

David M. L. Sills

2009-05-01T23:59:59.000Z

49

Integration of Variable Generation Forecasting into System Operations: Current Practices and Future Requirements  

Science Conference Proceedings (OSTI)

This project update provides the first output of the EPRI Bulk Renewable Integration Program Project P173-010, “Integration of Variable Generation Forecasts into System Operations.” This project, begun in 2013, aims to improve existing methods utilities/independent system operators (ISOs) use to integrate forecasts into system operations and develop new methods. This year’s goal was to identify current practices and future requirements. This was done by interacting with a wide ...

2013-12-11T23:59:59.000Z

50

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

E-Print Network (OSTI)

This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.

Dorta, Miguel

2012-05-01T23:59:59.000Z

51

Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study  

E-Print Network (OSTI)

(e.g., HVAC) for a specific building, optimizing control systems and strategies for a buildingPredicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study Richard building energy modeling suffers from several factors, in- cluding the large number of inputs required

Tennessee, University of

52

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

E-Print Network (OSTI)

Analysis: Studies in Residential Energy Demand. AcademicHousing Characteristics 1987, Residential Energy ConsumptionHousing Characteristics 1990, Residential Energy Consumption

Johnson, F.X.

2010-01-01T23:59:59.000Z

53

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

SciTech Connect

On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

Bolinger, Mark A.; Wiser, Ryan H.

2010-01-04T23:59:59.000Z

54

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

E-Print Network (OSTI)

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

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

55

Residential and Transport Energy Use in India: Past Trend and Future Outlook  

E-Print Network (OSTI)

16 Figure 10. Residential Primary Energy Use in 2000 and3. Fuel Consumption in the Residential Sector in 2005 in10 Table 6. Residential Activity

de la Rue du Can, Stephane

2009-01-01T23:59:59.000Z

56

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

E-Print Network (OSTI)

Residential Energy Consumption Survey, Human and Socialof Residential Building Energy Consumption in China Nan ZhouResidential Building Energy Consumption in China Nan Zhou*,

Zhou, Nan

2010-01-01T23:59:59.000Z

57

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

E-Print Network (OSTI)

The China Residential Energy Consumption Survey, Human andof Residential Building Energy Consumption in China Nan ZhouResidential Building Energy Consumption in China Nan Zhou*,

Zhou, Nan

2010-01-01T23:59:59.000Z

58

The future market for residential photovoltaic systems: New perspectives on the rooftop resource base  

Science Conference Proceedings (OSTI)

The resource base of one-family houses with roofs suitable for photovoltaic applications can be estimated using a new and efficient sampling strategy combined with a statistical model. Levels of residential housing density are good predictors of rooftop suitability and can be used to make out-of-sample forecasts for a variety of geographic areas. The model is particularly useful for making forecasts for census tracts within large metropolitan areas. A large-scale field survey of rooftops in southeastern Pennsylvania indicates that a much higher proportion of roofs are suitable for rooftop systems than previously thought (in the Northeast). The survey suggests important differences from previous assumptions for such roof characteristics as area, slope, aspect, and the frequency of roof obstructions. Overall, the rooftop resource base; this paradigm rests on the dramatic difference between the central city and the suburbs in the proportion of houses with suitable roofs. The survey also demonstrates the critical role played by landscaping practices within a single urban area.

Miller, K.R.

1992-01-01T23:59:59.000Z

59

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network (OSTI)

This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the U.S. residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute (McMenamin et al. 1992). In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70 % of electricity consumption and 30 % of natural gas consumption in the U.S. residential sector (EIA 1993). Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific

J. Hwang; Francis X. Johnson; Richard E. Brown; James W. Hanford; Jonathan G. Koomey

1994-01-01T23:59:59.000Z

60

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

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

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

62

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

DOE Green Energy (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

63

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

DOE Green Energy (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

64

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

Science Conference Proceedings (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

65

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

E-Print Network (OSTI)

revisions to the EIA’s natural gas price forecasts in AEOsolely on the AEO 2005 natural gas price forecasts willComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

66

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

67

Forecasting the Locations of Future Large Earthquakes: An Analysis and Verification  

E-Print Network (OSTI)

its uses in earthquake forecasting, Pure Appl. Geophys. 162,D.L. (2005), Earthquake forecasting and its veri?ca- tion,hazard assessment and forecasting, Pure Appl. Geophys. 157,

Shcherbakov, Robert; Turcotte, Donald L.; Rundle, John B.; Tiampo, Kristy F.; Holliday, James R.

2010-01-01T23:59:59.000Z

68

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

E-Print Network (OSTI)

revisions to the EIA’s natural gas price forecasts in AEOon the AEO 2005 natural gas price forecasts will likely onceComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

69

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from the AEO

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

70

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

the base-case natural gas price forecast, but to alsoof AEO 2010 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

71

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

the forecast. In 1978 the Natural Gas Policy Act was passedof Other Natural Gas Price Forecasts Researchers and policyresearchers and policy makers who utilize natural gas prices

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

72

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

73

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2008-01-01T23:59:59.000Z

74

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

75

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2009-01-01T23:59:59.000Z

76

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

E-Print Network (OSTI)

energy policy initiatives (EIA 1990). Utilities rely on end-use forecasting models in order to assess market trends

Johnson, F.X.

2010-01-01T23:59:59.000Z

77

Future Air Conditioning Energy Consumption in Developing Countriesand what can be done about it: The Potential of Efficiency in theResidential Sector  

SciTech Connect

The dynamics of air conditioning are of particular interestto energy analysts, both because of the high energy consumption of thisproduct, but also its disproportionate impact on peak load. This paperaddresses the special role of this end use as a driver of residentialelectricity consumption in rapidly developing economies. Recent historyhas shown that air conditioner ownership can grow grows more rapidly thaneconomic growth in warm-climate countries. In 1990, less than a percentof urban Chinese households owned an air conditioner; by 2003 this numberrose to 62 percent. The evidence suggests a similar explosion of airconditioner use in many other countries is not far behind. Room airconditioner purchases in India are currently growing at 20 percent peryear, with about half of these purchases attributed to the residentialsector. This paper draws on two distinct methodological elements toassess future residential air conditioner 'business as usual' electricityconsumption by country/region and to consider specific alternative 'highefficiency' scenarios. The first component is an econometric ownershipand use model based on household income, climate and demographicparameters. The second combines ownership forecasts and stock accountingwith geographically specific efficiency scenarios within a uniqueanalysis framework (BUENAS) developed by LBNL. The efficiency scenariomodule considers current efficiency baselines, available technologies,and achievable timelines for development of market transformationprograms, such as minimum efficiency performance standards (MEPS) andlabeling programs. The result is a detailed set of consumption andemissions scenarios for residential air conditioning.

McNeil, Michael A.; Letschert, Virginie E.

2007-05-01T23:59:59.000Z

78

Future Air Conditioning Energy Consumption in Developing Countriesand what can be done about it: The Potential of Efficiency in theResidential Sector  

SciTech Connect

The dynamics of air conditioning are of particular interestto energy analysts, both because of the high energy consumption of thisproduct, but also its disproportionate impact on peak load. This paperaddresses the special role of this end use as a driver of residentialelectricity consumption in rapidly developing economies. Recent historyhas shown that air conditioner ownership can grow grows more rapidly thaneconomic growth in warm-climate countries. In 1990, less than a percentof urban Chinese households owned an air conditioner; by 2003 this numberrose to 62 percent. The evidence suggests a similar explosion of airconditioner use in many other countries is not far behind. Room airconditioner purchases in India are currently growing at 20 percent peryear, with about half of these purchases attributed to the residentialsector. This paper draws on two distinct methodological elements toassess future residential air conditioner 'business as usual' electricityconsumption by country/region and to consider specific alternative 'highefficiency' scenarios. The first component is an econometric ownershipand use model based on household income, climate and demographicparameters. The second combines ownership forecasts and stock accountingwith geographically specific efficiency scenarios within a uniqueanalysis framework (BUENAS) developed by LBNL. The efficiency scenariomodule considers current efficiency baselines, available technologies,and achievable timelines for development of market transformationprograms, such as minimum efficiency performance standards (MEPS) andlabeling programs. The result is a detailed set of consumption andemissions scenarios for residential air conditioning.

McNeil, Michael A.; Letschert, Virginie E.

2007-05-01T23:59:59.000Z

79

Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets  

SciTech Connect

The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-06-30T23:59:59.000Z

80

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network (OSTI)

G. Koomey. 1994. Residential Appliance Data, Assumptions andunits) Table A 3 : Number of Appliances in Existing Homes (sector, including appliances and heating, ventilation, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

82

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark

2009-01-01T23:59:59.000Z

83

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

84

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

of AEO 2008 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2008-01-01T23:59:59.000Z

85

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

of AEO 2007 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

86

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

of AEO 2009 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2009-01-01T23:59:59.000Z

87

Life-cycle cost analysis of energy efficiency design options for residential furnaces and boilers  

E-Print Network (OSTI)

C-1 Residential Electricity Price Forecast (AEOC.1.2 Residential Electricity Price Forecast (AEO 2003) AEOdoes not require electricity price trends and discount

Lutz, James; Lekov, Alex; Whitehead, Camilla Dunham; Chan, Peter; Meyers, Steve; McMahon, James

2004-01-01T23:59:59.000Z

88

Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

approach to evaluating price risk would be to use suchthe base-case natural gas price forecast, but to alsorange of different plausible price projections, using either

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

89

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

this “hybrid” NYMEX-EIA gas price projection still does notcomparison with fixed- price renewable generation (becauseonly a portion of the gas price forecast – through 2010 –

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

90

Projected Regional Impacts of Appliance Efficiency Standards for the U.S. Residential Sector  

E-Print Network (OSTI)

Price. 1994. Baseline Data for the Residential Sector andDevelopment of a Residential Forecasting Database. LawrenceG . Koomey. 1994. Residential HVAC Data, Assumptions and

Koomey, J.G.

2010-01-01T23:59:59.000Z

91

Forecasting overview  

E-Print Network (OSTI)

Forecasting is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volume; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. Some things are easier to forecast than others. The time of the sunrise tomorrow morning can be forecast very precisely. On the other hand, currency exchange rates are very difficult to forecast with any accuracy. The predictability of an event or a quantity depends on how well we understand the factors that contribute to it, and how much unexplained variability is involved. Forecasting situations vary widely in their time horizons, factors determining actual outcomes, types of data patterns, and many other aspects. Forecasting methods can be very simple such as using the most recent observation as a forecast (which is called the “naïve method”), or highly complex such as neural nets and econometric systems of simultaneous equations. The

Rob J Hyndman

2009-01-01T23:59:59.000Z

92

Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices  

SciTech Connect

On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired or nuclear generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers; and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal, uranium, and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

Bolinger, Mark; Wiser, Ryan

2009-01-28T23:59:59.000Z

93

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

Science Conference Proceedings (OSTI)

On December 12, 2007, the reference-case projections from Annual Energy Outlook 2008 (AEO 2008) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof) or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers (though its appeal has diminished somewhat as prices have increased); and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

Bolinger, Mark A; Bolinger, Mark; Wiser, Ryan

2008-01-07T23:59:59.000Z

94

From: Mark Bolinger and Ryan Wiser, Berkeley Lab (LBNL) Subject: Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices Date: January 4, 2010 1. Introduction, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better

95

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

E-Print Network (OSTI)

to the EIA’s natural gas price forecasts in AEO 2004 and AEOcost comparisons of fixed-price renewable generationwith variable price gas-fired generation that are based

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

96

Using artificial neural networks to forecast the futures prices of crude oil  

Science Conference Proceedings (OSTI)

Crude oil is the commodity de jour and its pricing is of paramount importance to the layperson as well as to any responsible government. However, one of the main challenges facing econometric pricing models is the forecasting accuracy. ...

Hassan A. Khazem / A. K. Mazouz

2008-01-01T23:59:59.000Z

97

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.

98

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

Energy futures markets are ‘hubs’ that price and marketenergy price fluctuations. In theory, futures market pricesenergy prices, including most prominently, energy futures markets.

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

99

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

Science Conference Proceedings (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

100

Residential and Transport Energy Use in India: Past Trend and Future Outlook  

E-Print Network (OSTI)

Sectoral Trends and Future Outlook”. January 2007. LBNL-India: Past Trend and Future Outlook Stephane de la Rue duSectoral Trends and Future Outlook (Zhou et al. , 2007)

de la Rue du Can, Stephane

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

energy price fluctuations. In theory, futures market prices summarize privately available informationEnergy; Brookhaven National Laboratory Canadian Energy Research Institute U.S. Energy Information Administration Energy Marketsinformation about future energy prices, including most prominently, energy futures markets.

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

102

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Daily price history of 1st-nearby NYMEX natural gas futuresNatural Gas Futures Prices Figure 1 focuses on the historythe daily history of the average 5-year natural gas futures

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

103

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network (OSTI)

residential and commercial electricity demand forecasts. The23 Electricity Demandand commercial electricity demand per census division from

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

2005-01-01T23:59:59.000Z

104

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.

105

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

106

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

107

Residential and Transport Energy Use in India: Past Trend and Future Outlook  

SciTech Connect

The main contribution of this report is to characterize the underlying residential and transport sector end use energy consumption in India. Each sector was analyzed in detail. End-use sector-level information regarding adoption of particular technologies was used as a key input in a bottom-up modeling approach. The report looks at energy used over the period 1990 to 2005 and develops a baseline scenario to 2020. Moreover, the intent of this report is also to highlight available sources of data in India for the residential and transport sectors. The analysis as performed in this way reveals several interesting features of energy use in India. In the residential sector, an analysis of patterns of energy use and particular end uses shows that biomass (wood), which has traditionally been the main source of primary energy used in households, will stabilize in absolute terms. Meanwhile, due to the forces of urbanization and increased use of commercial fuels, the relative significance of biomass will be greatly diminished by 2020. At the same time, per household residential electricity consumption will likely quadruple in the 20 years between 2000 and 2020. In fact, primary electricity use will increase more rapidly than any other major fuel -- even more than oil, in spite of the fact that transport is the most rapidly growing sector. The growth in electricity demand implies that chronic outages are to be expected unless drastic improvements are made both to the efficiency of the power infrastructure and to electric end uses and industrial processes. In the transport sector, the rapid growth in personal vehicle sales indicates strong energy growth in that area. Energy use by cars is expected to grow at an annual growth rate of 11percent, increasing demand for oil considerably. In addition, oil consumption used for freight transport will also continue to increase .

de la Rue du Can, Stephane; Letschert, Virginie; McNeil, Michael; Zhou, Nan; Sathaye, Jayant

2009-03-31T23:59:59.000Z

108

Predicting Future Hourly Residential Electrical Consumption: A Machine Learning Case Study  

Science Conference Proceedings (OSTI)

Whole building input models for energy simulation programs are frequently created in order to evaluate specific energy savings potentials. They are also often utilized to maximize cost-effective retrofits for existing buildings as well as to estimate the impact of policy changes toward meeting energy savings goals. Traditional energy modeling suffers from several factors, including the large number of inputs required to characterize the building, the specificity required to accurately model building materials and components, simplifying assumptions made by underlying simulation algorithms, and the gap between the as-designed and as-built building. Prior works have attempted to mitigate these concerns by using sensor-based machine learning approaches to model energy consumption. However, a majority of these prior works focus only on commercial buildings. The works that focus on modeling residential buildings primarily predict monthly electrical consumption, while commercial models predict hourly consumption. This means there is not a clear indicator of which techniques best model residential consumption, since these methods are only evaluated using low-resolution data. We address this issue by testing seven different machine learning algorithms on a unique residential data set, which contains 140 different sensors measurements, collected every 15 minutes. In addition, we validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, using the original competition metrics. Our validation results confirm existing conclusions that Neural Network-based methods perform best on commercial buildings. However, the results from testing our residential data set show that Feed Forward Neural Networks, Support Vector Regression (SVR), and Linear Regression methods perform poorly, and that Hierarchical Mixture of Experts (HME) with Least Squares Support Vector Machines (LS-SVM) performs best - a technique not previously applied to this domain.

Edwards, Richard E [ORNL; New, Joshua Ryan [ORNL; Parker, Lynne Edwards [ORNL

2012-01-01T23:59:59.000Z

109

Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Daily price history of 1st-nearby NYMEX natural gas futuresthe daily history of the average 5-year natural gas futuresNatural Gas Futures Prices F igure 1 focuses on the history

Bolinger, Mark

2008-01-01T23:59:59.000Z

110

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

Daily price history of 1st-nearby NYMEX natural gas futuresNatural Gas Futures Prices F igure 1 focuses on the historynatural gas prices. Figure 1 shows the daily price history

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

111

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

Appendix A.1 Natural Gas Price Data for Futures Market andSTEO Error A.1 Natural Gas Price Data for Futures Market andforecasts for natural gas prices as reported by the Energy

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

112

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.

113

Constraints on primordial non-Gaussianity from WMAP7 and luminous red galaxies power spectrum and forecast for future surveys  

Science Conference Proceedings (OSTI)

We place new constraints on the primordial local non-Gaussianity parameter f{sub NL} using recent cosmic microwave background anisotropy and galaxy clustering data. We model the galaxy power spectrum according to the halo model, accounting for a scale-dependent bias correction proportional to f{sub NL}/k{sup 2}. We first constrain f{sub NL} in a full 13 parameters analysis that includes 5 parameters of the halo model and 7 cosmological parameters. Using the WMAP7 CMB data and the SDSS DR4 galaxy power spectrum, we find f{sub NL}=171{sub -139}{sup +140} at 68% C.L. and -69forecast the constraints on f{sub NL} from future surveys as EUCLID and from CMB missions as Planck showing that their combined analysis could detect f{sub NL{approx}}5.

De Bernardis, Francesco [Physics Department and INFN, Universita di Roma 'La Sapienza', Ple Aldo Moro 2, 00185, Rome (Italy); Center for Cosmology, Department of Physics and Astronomy, University of California Irvine, Irvine, California 92697 (United States); Serra, Paolo; Cooray, Asantha [Center for Cosmology, Department of Physics and Astronomy, University of California Irvine, Irvine, California 92697 (United States); Melchiorri, Alessandro [Physics Department and INFN, Universita di Roma 'La Sapienza', Ple Aldo Moro 2, 00185, Rome (Italy)

2010-10-15T23:59:59.000Z

114

Energy Data Sourcebook for the U.S. Residential Sector  

NLE Websites -- All DOE Office Websites (Extended Search)

Data Sourcebook for the U.S. Residential Sector Data Sourcebook for the U.S. Residential Sector Title Energy Data Sourcebook for the U.S. Residential Sector Publication Type Report LBNL Report Number LBNL-40297 Year of Publication 1997 Authors Wenzel, Thomas P., Jonathan G. Koomey, Gregory J. Rosenquist, Marla C. Sanchez, and James W. Hanford Date Published 09/1997 Publisher Lawrence Berkeley National Laboratory City Berkeley, CA ISBN Number LBNL-40297, UC-1600 Keywords Enduse, Energy End-Use Forecasting, EUF Abstract Analysts assessing policies and programs to improve energy efficiency in the residential sector require disparate input data from a variety of sources. This sourcebook, which updates a previous report, compiles these input data into a single location. The data provided include information on end-use unit energy consumption (UEC) values of appliances and equipment; historical and current appliance and equipment market shares; appliance and equipment efficiency and sales trends; appliance and equipment efficiency standards; cost vs. efficiency data for appliances and equipment; product lifetime estimates; thermal shell characteristics of buildings; heating and cooling loads; shell measure cost data for new and retrofit buildings; baseline housing stocks; forecasts of housing starts; and forecasts of energy prices and other economic drivers. This report is the essential sourcebook for policy analysts interested in residential sector energy use. The report can be downloaded from the Web at http://enduse.lbl.gov/Projects/RED.html. Future updates to the report, errata, and related links, will also be posted at this address.

115

A model for forecasting future air travel demand on the North Atlantic  

E-Print Network (OSTI)

Introduction: One of the key problems in the analysis and planning of any transport properties and facilities is estimating the future volume of traffic that may be expected to use these properties and facilities. Estimates ...

Taneja, Nawal K.

1971-01-01T23:59:59.000Z

116

Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets  

E-Print Network (OSTI)

Update on Petroleum, Natural Gas, Heating Oil and Gasoline.of the Market for Natural Gas Futures. Energy Journal 16 (Modeling Forum. 2003. Natural Gas, Fuel Diversity and North

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-01-01T23:59:59.000Z

117

Forecasting future economic growth : the term structure of interest rates, volatility and inflation as leading indicators  

E-Print Network (OSTI)

The broad literature documents the empirical regularity that slope of the term structure of interest rates is a reliable predictor of future real economic activity. Steeper slopes presage increasing growth, and downward ...

Khait, Maria

2012-01-01T23:59:59.000Z

118

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

119

Planning and decision making about the future care of older group home residents and transition to residential  

E-Print Network (OSTI)

to residential aged carejir_1297 1..13 C. Bigby,1 B. Bowers2 & R. Webber3 1 School of SocialWork and Social residents and the decisions made that a move to residential aged care was necessary. Methods Grounded to a residential aged care facility was neces- sary were made in haste and seen as a fait accompli to involved

Wisconsin at Madison, University of

120

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

Note: This page contains sample records for the topic "forecasts future residential" 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

Forecast constraints on cosmic strings from future CMB, pulsar timing and gravitational wave direct detection experiments  

E-Print Network (OSTI)

We study future observational constraints on cosmic string parameters from various types of next-generation experiments: direct detection of gravitational waves (GWs), pulsar timing array, and the cosmic microwave background (CMB). We consider both GW burst and stochastic GW background searches by ground- and space-based interferometers as well as GW background detection in pulsar timing experiments. We also consider cosmic string contributions to the CMB temperature and polarization anisotropies. These different types of observations offer independent probes of cosmic strings and may enable us to investigate cosmic string properties if the signature is detected. In this paper, we evaluate the power of future experiments to constrain cosmic string parameters, such as the string tension Gmu, the initial loop size alpha, and the reconnection probability p, by performing Fisher information matrix calculations. We find that combining the information from the different types of observations breaks parameter degeneracies and provides more stringent constraints on the parameters. We also find future space-borne interferometers independently provide a highly precise determination of the parameters.

Sachiko Kuroyanagi; Koichi Miyamoto; Toyokazu Sekiguchi; Keitaro Takahashi; Joseph Silk

2012-10-10T23:59:59.000Z

122

FORECASTING COSMOLOGICAL PARAMETER CONSTRAINTS FROM NEAR-FUTURE SPACE-BASED GALAXY SURVEYS  

Science Conference Proceedings (OSTI)

The next generation of space-based galaxy surveys is expected to measure the growth rate of structure to a level of about one percent over a range of redshifts. The rate of growth of structure as a function of redshift depends on the behavior of dark energy and so can be used to constrain parameters of dark energy models. In this work, we investigate how well these future data will be able to constrain the time dependence of the dark energy density. We consider parameterizations of the dark energy equation of state, such as XCDM and {omega}CDM, as well as a consistent physical model of time-evolving scalar field dark energy, {phi}CDM. We show that if the standard, specially flat cosmological model is taken as a fiducial model of the universe, these near-future measurements of structure growth will be able to constrain the time dependence of scalar field dark energy density to a precision of about 10%, which is almost an order of magnitude better than what can be achieved from a compilation of currently available data sets.

Pavlov, Anatoly; Ratra, Bharat [Department of Physics, Kansas State University, 116 Cardwell Hall, Manhattan, KS 66506 (United States); Samushia, Lado, E-mail: pavlov@phys.ksu.edu, E-mail: ratra@phys.ksu.edu, E-mail: lado.samushia@port.ac.uk [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth PO1 3FX (United Kingdom)

2012-11-20T23:59:59.000Z

123

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.

124

Residential and Transport Energy Use in India: Past Trend and...  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential and Transport Energy Use in India: Past Trend and Future Outlook Title Residential and Transport Energy Use in India: Past Trend and Future Outlook Publication Type...

125

Forecast Correlation Coefficient Matrix of Stock Returns in Portfolio Analysis  

E-Print Network (OSTI)

Unadjusted Forecasts . . . . . . . . . . . . . . . .Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . .Unadjusted Forecasts . . . . . . . . . . . . . . . . . . .

Zhao, Feng

2013-01-01T23:59:59.000Z

126

Synoptic Forecasting of the Oceanic Mixed Layer Using the Navy's Operational Environmental Data Base: Present Capabilities and Future Applications  

Science Conference Proceedings (OSTI)

A synoptic forecast model of the oceanic mixed layer has been developed for operational use at the U.S. Navy's Fleet Numerical Oceanography Center (FNOC), Monterey, Calif. The potential success of this model depends critically on the quality of ...

R. Michael Clancy; Paul J. Martin

1981-06-01T23:59:59.000Z

127

Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research  

Science Conference Proceedings (OSTI)

Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of ...

Francisco J. Meza; James W. Hansen; Daniel Osgood

2008-05-01T23:59:59.000Z

128

Orphan drugs : future viability of current forecasting models, in light of impending changes to influential market factors  

E-Print Network (OSTI)

Interviews were conducted to establish a baseline for how orphan drug forecasting is currently undertaken by financial market and industry analysts with the intention of understanding the variables typically accounted for ...

Gottlieb, Joshua

2011-01-01T23:59:59.000Z

129

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

130

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff members in the Demand prepared the residential sector forecast. Mohsen Abrishami prepared the commercial sector forecast. Lynn

131

Propane demand modeling for residential sectors- A regression analysis.  

E-Print Network (OSTI)

??This thesis presents a forecasting model for the propane consumption within the residential sector. In this research we explore the dynamic behavior of different variables… (more)

Shenoy, Nitin K.

2011-01-01T23:59:59.000Z

132

Business forecasting methods  

E-Print Network (OSTI)

Forecasting is a common statistical task in business, where it helps inform decisions about scheduling of production, transportation and personnel, and provides a guide to long-term strategic planning. However, business forecasting is often done poorly and is frequently confused with planning and goals. They are three different things. Forecasting is about predicting the future as accurately as possible, given all the information available including historical data and knowledge of any future events that might impact the forecasts. Goals are what you would like to happen. Goals should be linked to forecasts and plans, but this does not always occur. Too often, goals are set without any plan for how to achieve them, and no forecasts for whether they are realistic. Planning is a response to forecasts and goals. Planning involves determining the appropriate actions that are required to make your forecasts match your goals. Forecasting should be an integral part of the decision-making activities of management, as it can play an important role in many areas of a company. Modern organizations require short-, medium- and long-term forecasts, depending on the specific application.

Rob J Hyndman

2009-01-01T23:59:59.000Z

133

Forecast Technical Document Forecast Types  

E-Print Network (OSTI)

Forecast Technical Document Forecast Types A document describing how different forecast types are implemented in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Forecast Types Background Different `types' of forecast are possible for a specified area

134

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect

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

135

Savings from energy efficient windows: Current and future savings from new fenestration technologies in the residential market  

SciTech Connect

Heating and cooling energy lost through windows in the residential sector (estimated at two-thirds of the energy lost through windows in all sectors) currently accounts for 3 percent (or 2.8 quads) of total US energy use, costing over $26 billion annually in energy bills. Installation of energy-efficient windows is acting to reduce the amount of energy lost per unit window area. Installation of more energy efficient windows since 1970 has resulted in an annual savings of approximately 0.6 quads. If all windows utilized existing cost effective energy conserving technologies, then residential window energy losses would amount to less than 0.8 quads, directly saving $18 billion per year in avoided energy costs. The nationwide installation of windows that are now being developed could actually turn this energy loss into a net energy gain. Considering only natural replacement of windows and new construction, appropriate fenestration policies could help realize this potential by reducing annual residential window energy losses to 2.2 quids by the year 2012, despite a growing housing stock.

Frost, K.; Arasteh, D.; Eto, J.

1993-04-01T23:59:59.000Z

136

RACORO Forecasting  

NLE Websites -- All DOE Office Websites (Extended Search)

Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance from Norman NWS enhanced pages and discussions NAM-WRF updated...

137

Residential Performance  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential Performance: guidelines, analysis and measurements of window and skylight performance Windows in residential buildings consume approximately 2% of all the energy used...

138

Forecasting exurban development to evaluate the influence of land-use policies on wildland and farmland conservation  

E-Print Network (OSTI)

Planning Vol 1 (2005) 40—57 Forecasting exurban developmentof general plans for forecasting future development can be

Merenlender, Adina M.; Brooks, Colin; Shabazian, David; Gao, Shengyi; Johnston, Robert A.

2005-01-01T23:59:59.000Z

139

Uses and Applications of Climate Forecasts for Power Utilities  

Science Conference Proceedings (OSTI)

The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-...

Stanley A. Changnon; Joyce M. Changnon; David Changnon

1995-05-01T23:59:59.000Z

140

Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

We have shown that it is possible to predict the skill of numerical weather forecasts—a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average ...

Eugenia Kalnay; Amnon Dalcher

1987-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Forecast Combinations  

E-Print Network (OSTI)

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this chapter we analyze theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models’ forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where thenumbersofmodelsislargerelativetothe available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts. Key words: Forecast combinations; pooling and trimming; shrinkage methods; model misspecification, diversification gains

Allan Timmermann; Jel Codes C

2006-01-01T23:59:59.000Z

142

Procedures and Standards for Residential Ventilation System Commissioning:  

NLE Websites -- All DOE Office Websites (Extended Search)

Procedures and Standards for Residential Ventilation System Commissioning: Procedures and Standards for Residential Ventilation System Commissioning: An Annotated Bibliography Title Procedures and Standards for Residential Ventilation System Commissioning: An Annotated Bibliography Publication Type Report LBNL Report Number LBNL-6142E Year of Publication 2013 Authors J. Chris Stratton, and Craig P. Wray Keywords ASHRAE 62.2, commissioning, procedures, residential, standards, ventilation Abstract Beginning with the 2008 version of Title 24, new homes in California must comply with ANSI/ASHRAE Standard 62.2-2007 requirements for residential ventilation. Where installed, the limited data available indicate that mechanical ventilation systems do not always perform optimally or even as many codes and forecasts predict. Commissioning such systems when they are installed or during subsequent building retrofits is a step towards eliminating deficiencies and optimizing the tradeoff between energy use and acceptable IAQ. Work funded by the California Energy Commission about a decade ago at Berkeley Lab documented procedures for residential commissioning, but did not focus on ventilation systems. Since then, standards and approaches for commissioning ventilation systems have been an active area of work in Europe. This report describes our efforts to collect new literature on commissioning procedures and to identify information that can be used to support the future development of residential-ventilation-specific procedures and standards. We recommend that a standardized commissioning process and a commissioning guide for practitioners be developed, along with a combined energy and IAQ benefit assessment standard and tool, and a diagnostic guide for estimating continuous pollutant emission rates of concern in residences (including a database that lists emission test data for commercially-available labeled products).

143

Please cite this article in press as: R.E. Edwards, et al., Predicting future hourly residential electrical consumption: A machine learning case study, Energy Buildings (2012), doi:10.1016/j.enbuild.2012.03.010  

E-Print Network (OSTI)

-offs in the building design process, sizing components (e.g., HVAC) for a specific building, optimizing control systemsPlease cite this article in press as: R.E. Edwards, et al., Predicting future hourly residential.03.010 ARTICLE IN PRESSG Model ENB-3661; No.of Pages13 Energy and Buildings xxx (2012) xxx­xxx Contents lists

Parker, Lynne E.

144

Short-Term Energy Outlook Model Documentation: Regional Residential Heating Oil Price Model  

Reports and Publications (EIA)

The regional residential heating oil price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 census regions: Northeast, South, Midwest, and West.

Information Center

2009-11-09T23:59:59.000Z

145

Short-Term Energy Outlook Model Documentation: Regional Residential Propane Price Model  

Reports and Publications (EIA)

The regional residential propane price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 census regions: Northeast, South, Midwest, and West.

Information Center

2009-11-09T23:59:59.000Z

146

Projecting market demand for residential heat pumps  

SciTech Connect

Primarily because of technological improvements and sharp increases in energy prices after the 1970s energy crises, the sale of residential electric heat pumps rose ninefold from 1970 to 1983. This report describes current and future market demand for heat pumps used for space heating and cooling. A three-step approach was followed. In the first step, the historical growth of residential electric heat pumps was analyzed, and factors that may have affected market growth were examined. Also examined were installation trends of heat pumps in new single-family and multifamily homes. A market segmentation analysis was used to estimate market size by categories. In the second step, several methods for forecasting future market demand were reviewed and evaluated to select the most suitable one for this study. The discrete-choice approach was chosen. In the third step, a market penetration model based on selected discrete-choice methods was developed to project heat pump demand in key market segments such as home type (single-family or multifamily), new or existing construction, and race-ethnic origin of household (black, Hispanic, or white).

Teotia, A.P.S.; Raju, P.S.; Karvelas, D.; Anderson, J.

1987-04-01T23:59:59.000Z

147

Long range forecast of power demands on the Baltimore Gas and Electric Company system. Volume 1  

SciTech Connect

The report presents the results of an econometric forecast of peak and electric power demands for the Baltimore Gas and Electric Company (BGandE) through the year 2003. The report describes the methodology, the results of the econometric estimations and associated summary statistics, the forecast assumptions, and the calculated forecasts of energy usage and peak demand. Separate models were estimated for summer and winter residential electricity usage in both Baltimore city and the non-city portion of the BGandE service area. Equations were also estimated for commercial energy usage, industrial usage, streetlighting, and for losses plus Company use. Non-econometric techniques were used to estimate future energy use by Bethlehem Steel Corporation's Sparrows Point plant in Baltimore County, Conrail, and the Baltimore Mass Transit Administration underground rail system. Models of peak demand for summer and winter were also estimated.

Estomin, S.L.; Kahal, M.I.

1985-09-01T23:59:59.000Z

148

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

149

Residential Buildings  

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

Residential Residential Residential Buildings Residential buildings-such as single family homes, townhomes, condominiums, and apartment buildings-are all covered by the Residential Energy Consumption Survey (RECS). See the RECS home page for further information. However, buildings that offer multiple accomodations such as hotels, motels, inns, dormitories, fraternities, sororities, convents, monasteries, and nursing homes, residential care facilities are considered commercial buildings and are categorized in the CBECS as lodging. Specific questions may be directed to: Joelle Michaels joelle.michaels@eia.doe.gov CBECS Manager Release date: January 21, 2003 Page last modified: May 5, 2009 10:18 AM http://www.eia.gov/consumption/commercial/data/archive/cbecs/pba99/residential.html

150

Future Power Systems 20: The Smart Enterprise, its Objective...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

0: The Smart Enterprise, its Objective and Forecasting. Future Power Systems 20: The Smart Enterprise, its Objective and Forecasting. Future Power Systems 20: The Smart Enterprise,...

151

Optimizing Energy Savings from Direct-DC in U.S. Residential Buildings  

E-Print Network (OSTI)

residential electricity consumption, a simplified approach was used to determine plausible future penetration rates

Garbesi, Karina

2012-01-01T23:59:59.000Z

152

Performance Criteria for Residential Zero Energy Windows  

E-Print Network (OSTI)

LaFrance. 2006. “Zero Energy Windows. ” Proceedings of the2003. “Future Advanced Windows for Zero-Energy Homes. ”and cooling energy use of windows in residential buildings—

Arasteh, Dariush; Goudey, Howdy; Huang, Joe; Kohler, Christian; Mitchell, Robin

2006-01-01T23:59:59.000Z

153

EIA - The National Energy Modeling System: An Overview 2003-Residential  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module The National Energy Modeling System: An Overview 2003 Residential Demand Module Figure 5. Residential Demand Module Structure. Need help, contact the National Energy Information Center at 202-586-8800. Residential Demand Module Table. Need help, contact the National Energy Information Center at 202-586-8800. NEMS Residential Module Equipment Summary Table. Need help, contact the National Energy Information Center at 202-586-8800. Characteristics of Selected Equipment Table. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The 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

154

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect

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

155

Residential Buildings  

NLE Websites -- All DOE Office Websites (Extended Search)

Apartment building exterior and interior Apartment building exterior and interior Residential Buildings EETD's research in residential buildings addresses problems associated with whole-building integration involving modeling, measurement, design, and operation. Areas of research include the movement of air and associated penalties involving distribution of pollutants, energy and fresh air. Contacts Max Sherman MHSherman@lbl.gov (510) 486-4022 Iain Walker ISWalker@lbl.gov (510) 486-4692 Links Residential Building Systems Group Batteries and Fuel Cells Buildings Energy Efficiency Applications Commercial Buildings Cool Roofs and Heat Islands Demand Response Energy Efficiency Program and Market Trends High Technology and Industrial Systems Lighting Systems Residential Buildings Simulation Tools Sustainable Federal Operations

156

Regional Residential  

Gasoline and Diesel Fuel Update (EIA)

upward pressure from crude oil markets, magnified by a regional shortfall of heating oil supplies, residential prices rose rapidly to peak February 7. The problem was...

157

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

the U.S. Department of Energy (US DOE). It is the mostmodels that forecast US residential energy consumption bySurveys of sector energy use (US DOE 1990a; A G A 1991; EEI

Wenzel, T.P.

2010-01-01T23:59:59.000Z

158

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

5 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1 Francis X. Johnson, Richard E. Brown, James W. Hanford, Alan H. Sanstad and...

159

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,

160

Energy data sourcebook for the US residential sector  

Science Conference Proceedings (OSTI)

Analysts assessing policies and programs to improve energy efficiency in the residential sector require disparate input data from a variety of sources. This sourcebook, which updates a previous report, compiles these input data into a single location. The data provided include information on end-use unit energy consumption (UEC) values of appliances and equipment efficiency; historical and current appliance and equipment market shares; appliances and equipment efficiency and sales trends; appliance and equipment efficiency standards; cost vs. efficiency data for appliances and equipment; product lifetime estimates; thermal shell characteristics of buildings; heating and cooling loads; shell measure cost data for new and retrofit buildings; baseline housing stocks; forecasts of housing starts; and forecasts of energy prices and other economic drivers. This report is the essential sourcebook for policy analysts interested in residential sector energy use. The report can be downloaded from the Web at http://enduse.lbl. gov/Projects/RED.html. Future updates to the report, errata, and related links, will also be posted at this address.

Wenzel, T.P.; Koomey, J.G.; Sanchez, M. [and others

1997-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

E-Print Network (OSTI)

million Homes: Drivers and Outlook for Residential Energymillion Homes: Drivers and Outlook for Residential Energyconsumption, future outlook, end-use, bottom-up analysis

Zhou, Nan

2010-01-01T23:59:59.000Z

162

Uncertainties in forecasting future climate  

SciTech Connect

The increasing atmospheric concentrations of carbon dioxide, methane, chlorofluorocarbons, and other trace gases (collectively, greenhouse gases) pose a three-part challenge: (1) What the changes to atmospheric composition and the climate system will be; (2) What impacts (both detrimental and beneficial) these changes will induce on the biosphere and natural and societal resources; and (3) What the appropriate response, if any, might be when considering the changes themselves, the resulting impacts, and the benefits and other impacts of the activities generating the emissions. This brief summary will address only areas of agreement and areas of uncertainty related to the first challenge.

MacCracken, M.C.

1990-11-01T23:59:59.000Z

163

Revised Draft Fuel Price Forecasts for the Draft  

E-Print Network (OSTI)

Natural gas prices, as well as oil and coal prices, are forecast using an Excel spreadsheet model at this time, natural gas prices are forecast in more detail than oil and coal prices. Residential in the industrial boiler fuel market to help keep natural gas prices low. Continuing declines in coal prices coupled

164

DRAFT FUEL PRICE FORECASTS FOR THE 5TH  

E-Print Network (OSTI)

. Forecast Methods Natural gas prices, as well as oil and coal prices, are forecast using an Excel in more detail than oil and coal prices. Residential and commercial sector retail natural gas prices market to help keep natural gas prices low. Continuing declines in coal prices coupled with improved

165

EUROCONTROL EUROCONTROL Long-Term Forecast: IFR Flight Movements 2010-2030  

E-Print Network (OSTI)

published in September 2010 (Ref.1). This forecast replaces the EUROCONTROL Long-Term Forecast issued in November 2008. The forecast uses four scenarios to explore the future of the aviation and the risks that lie

Flight Movements

2010-01-01T23:59:59.000Z

166

State-of-the art of freight forecast modeling: lessons learned and the road ahead  

E-Print Network (OSTI)

of-the art of freight forecast modeling: lessons learned andof goods as well as to forecast the expected future truckused for the short-term forecasts of freight volumes on

Chow, Joseph Y.; Yang, Choon Heon; Regan, Amelia C.

2010-01-01T23:59:59.000Z

167

Solar forecasting review  

E-Print Network (OSTI)

of Solar Forecasting . . . . . . . . . 2.4.1 Solarbudget at the foundation of satellite based forecastingWeather Research and Forecasting (WRF) Model 7.1 Global

Inman, Richard Headen

2012-01-01T23:59:59.000Z

168

Extended-Range Probability Forecasts Based on Dynamical Model Output  

Science Conference Proceedings (OSTI)

A probability forecast has advantages over a deterministic forecast as the former offers information about the probabilities of various possible future states of the atmosphere. As physics-based numerical models find their success in modern ...

Jianfu Pan; Huug van den Dool

1998-12-01T23:59:59.000Z

169

A web-based Hong Kong tourism demand forecasting system  

Science Conference Proceedings (OSTI)

Accurate predictions of future business activities are important for business decision-making. As a consequence, powerful and simple forecasting processes are urgently pursued by decision-makers. This study presents a tourism demand forecasting system ...

Haiyan Song; Zixuan Gao; Xinyan Zhang; Shanshan Lin

2012-04-01T23:59:59.000Z

170

Potential Economic Value of Ensemble-Based Surface Weather Forecasts  

Science Conference Proceedings (OSTI)

The possible economic value of the quantification of uncertainty in future ensemble-based surface weather forecasts is investigated using a formal, idealized decision model. Current, or baseline, weather forecasts are represented by probabilistic ...

Daniel S. Wilks; Thomas M. Hamill

1995-12-01T23:59:59.000Z

171

NFI Forecasts Methodology NFI Forecasts Methodology  

E-Print Network (OSTI)

NFI Forecasts Methodology NFI Forecasts Methodology Overview Issued by: National Forest Inventory.brewer@forestry.gsi.gov.uk Website: www.forestry.gov.uk/inventory 1 NFI Softwood Forecasts Methodology Overview #12;NFI Forecasts ........................................................................................................4 Rationale behind the new approach to the GB Private sector production forecast ........4 Volume

172

Forecast Technical Document Restocking in the Forecast  

E-Print Network (OSTI)

Forecast Technical Document Restocking in the Forecast A document describing how restocking of felled areas is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the forecast Background During the period of a production forecast it is assumed that, as forest sub

173

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS FORECAST IMPROVEMENTS  

E-Print Network (OSTI)

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS BRISBANE FORECAST IMPROVEMENTS The Bureau of Meteorology is progressively upgrading its forecast system to provide more detailed forecasts across Australia. From October 2013 new and improved 7 day forecasts will be introduced for Brisbane, Gold Coast

Greenslade, Diana

174

Energy conservation and official UK energy forecasts  

SciTech Connect

Behind the latest United Kingdom (UK) official forecasts of energy demand are implicit assumptions about future energy-price elasticities. Mr. Pearce examines the basis of the forecasts and finds that the long-term energy-price elasticities that they imply are two or three times too low. The official forecasts substantially understate the responsiveness of demand to energy price rises. If more-realistic price elasticities were assumed, the official forecasts would imply a zero primary energy-demand growth to 2000. This raises the interesting possibility of a low energy future being brought about entirely by market forces. 15 references, 3 tables.

Pearce, D.

1980-09-01T23:59:59.000Z

175

A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting  

Science Conference Proceedings (OSTI)

Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The ...

Lan Cuo; Thomas C. Pagano; Q. J. Wang

2011-10-01T23:59:59.000Z

176

Another Approach to Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

The skill of a medium-range numerical forecast can fluctuate widely from day to day. Providing an a priori estimate of the skill of the forecast is therefore important. Existing approaches include Monte Carlo Forecasting and Lagged Average ...

W. Y. Chen

1989-02-01T23:59:59.000Z

177

Residential Buildings  

NLE Websites -- All DOE Office Websites (Extended Search)

Exterior and interior of apartment building Exterior and interior of apartment building Residential Buildings The study of ventilation in residential buildings is aimed at understanding the role that air leakage, infiltration, mechanical ventilation, natural ventilation and building use have on providing acceptable indoor air quality so that energy and related costs can be minimized without negatively impacting indoor air quality. Risks to human health and safety caused by inappropriate changes to ventilation and air tightness can be a major barrier to achieving high performance buildings and must be considered.This research area focuses primarily on residential and other small buildings where the interaction of the envelope is important and energy costs are dominated by space conditioning energy rather than air

178

Guidelines for residential commissioning  

E-Print Network (OSTI)

Potential Benefits of Commissioning California Homes”.Delp. 2000. “Residential Commissioning: A Review of Relatedfor Evaluating Residential Commissioning Metrics” Lawrence

Wray, Craig P.; Walker, Iain S.; Sherman, Max H.

2003-01-01T23:59:59.000Z

179

The Boom of Electricity Demand in the Residential Sector in the Developing World and the Potential for Energy Efficiency  

E-Print Network (OSTI)

with Residential Electricity Demand in India's Future - HowThe Boom of Electricity Demand in the Residential Sector instraightforward. Electricity demand per end use and region

Letschert, Virginie

2010-01-01T23:59:59.000Z

180

Evaluation of evolving residential electricity tariffs  

Science Conference Proceedings (OSTI)

Residential customers in California's Pacific Gas and Electric (PG&E) territory have seen several electricity rate structure changes in the past decade. This poster: examines the history of the residential pricing structure and key milestones; summarizes and analyzes the usage between 2006 and 2009 for different baseline/climate areas; discusses the residential electricity Smart Meter roll out; and compares sample bills for customers in two climates under the current pricing structure and also the future time of use (TOU) structure.

Lai, Judy; DeForest, Nicholas; Kiliccote, Sila; Stadler, Michael; Marnay, Chris; Donadee, Jon

2011-05-15T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

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

182

The Forecast Gap: Linking Forwards and Forecasts  

Science Conference Proceedings (OSTI)

This report addresses a common problem in price forecasting: What to do when confronted with a persistent gap between results obtained from a structural forecast model and actual forward or spot prices? The report examines examples taken from natural gas and electric power forecasts and presents a novel approach to closing this “forecast gap.” Inspection reveals that the ratio of actual prices to forecast prices often exhibits stochastic movements that resemble those of commodity price movements. By usin...

2008-12-15T23:59:59.000Z

183

Earnings forecast bias -a statistical analysis Franois Dossou  

E-Print Network (OSTI)

Earnings forecast bias - a statistical analysis François Dossou Sandrine Lardic** Karine Michalon' earnings forecasts is an important aspect of research for different reasons: Many empirical studies employ analysts' consensus forecasts as a proxy for the market's expectations of future earnings in order

Paris-Sud XI, Université de

184

Distribution Based Data Filtering for Financial Time Series Forecasting  

E-Print Network (OSTI)

of stock prices, which aims to forecast the future values of the price of a stock, in order to obtain/selling strategies to gain competitive advantage. Classic and popular methods for stock price forecasting [3Distribution Based Data Filtering for Financial Time Series Forecasting Goce Ristanoski1 , James

Bailey, James

185

Draft Forecast of Electricity Demand for the 5th  

E-Print Network (OSTI)

products has been below the medium-low. Future natural gas prices are expected to be higher in this power's draft natural gas price forecasts. The medium natural gas price forecast for this plan in 2015 is about Council Document 2001-23, sited above. #12;DRAFT DRAFT DRAFT 11 Table 1 Natural Gas Price Forecasts

186

River flow forecasting with constructive neural network  

Science Conference Proceedings (OSTI)

In utilities using a mixture of hydroelectric and non-hydroelectric power, the economics of the hydroelectric plants depend upon the reservoir height and the inflow into the reservoir for several months into the future. Accurate forecasts of reservoir ...

Mêuser Valença; Teresa Ludermir; Anelle Valença

2005-12-01T23:59:59.000Z

187

Forecasting in Meteorology  

Science Conference Proceedings (OSTI)

Public weather forecasting heralded the beginning of modern meteorology less than 150 years ago. Since then, meteorology has been largely a forecasting discipline. Thus, forecasting could have easily been used to test and develop hypotheses, ...

C. S. Ramage

1993-10-01T23:59:59.000Z

188

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

SciTech Connect

China's rapid economic expansion has propelled it to the rank of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modelling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities. From this analysis, we can conclude that Chinese residential energy consumption will more than double by 2020, from 6.6 EJ in 2000 to 15.9 EJ in 2020. This increase will be driven primarily by urbanization, in combination with increases in living standards. In the urban and higher income Chinese households of the future, most major appliances will be common, and heated and cooled areas will grow on average. These shifts will offset the relatively modest efficiency gains expected according to current government plans and policies already in place. Therefore, levelling and reduction of growth in residential energy demand in China will require a new set of more aggressive efficiency policies.

Zhou, Nan; McNeil, Michael A.; Levine, Mark

2009-06-01T23:59:59.000Z

189

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

SciTech Connect

China's rapid economic expansion has propelled it to the rank of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modelling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities. From this analysis, we can conclude that Chinese residential energy consumption will more than double by 2020, from 6.6 EJ in 2000 to 15.9 EJ in 2020. This increase will be driven primarily by urbanization, in combination with increases in living standards. In the urban and higher income Chinese households of the future, most major appliances will be common, and heated and cooled areas will grow on average. These shifts will offset the relatively modest efficiency gains expected according to current government plans and policies already in place. Therefore, levelling and reduction of growth in residential energy demand in China will require a new set of more aggressive efficiency policies.

Zhou, Nan; McNeil, Michael A.; Levine, Mark

2009-06-01T23:59:59.000Z

190

Impact of Early Forecast Information Sharing on Manufacturers with Capacity Uncertainty  

E-Print Network (OSTI)

251 Impact of Early Forecast Information Sharing on Manufacturers with Capacity Uncertainty of future demand. Advanced forecast information sharing between buyer and seller about these demand patterns) manufacturers receive an early rough forecast with a deterministic due date, however, forecast revisions

Chinnam, Ratna Babu

191

Maintaining the Role of Humans in the Forecast Process: Analyzing the Psyche of Expert Forecasters  

Science Conference Proceedings (OSTI)

The Second Forum on the Future Role of the Human in the Forecast Process occurred on 2–3 August 2005 at the American Meteorological Society's Weather Analysis and Forecasting Conference in Washington, D.C. The forum consisted of three sessions. ...

Neil A. Stuart; David M. Schultz; Gary Klein

2007-12-01T23:59:59.000Z

192

Future Air Conditioning Energy Consumption in Developing Countries and what can be done about it: The Potential of Efficiency in the Residential Sector  

E-Print Network (OSTI)

Henderson (2005) Home air conditioning in Europe – how muchA.A. Pavlova ( 2003). Air conditioning market saturation and+ paper 6,306 Future Air Conditioning Energy Consumption in

McNeil, Michael A.; Letschert, Virginie E.

2008-01-01T23:59:59.000Z

193

Residential Sector Demand Module 1998, Model Documentation  

Reports and Publications (EIA)

This is the fourth edition of the Model Documentation Report: Residential Sector DemandModule of the National Energy Modeling System (NEMS). It reflects changes made to themodule over the past year for the Annual Energy Outlook 1998. Since last year, severalnew end-use services were added to the module, including: Clothes washers,dishwashers, furnace fans, color televisions, and personal computers. Also, as with allNEMS modules, the forecast horizon has been extended to the year 2020.

John H. Cymbalsky

1998-01-01T23:59:59.000Z

194

Appendix A: Fuel Price Forecast Introduction..................................................................................................................................... 1  

E-Print Network (OSTI)

Appendix A: Fuel Price Forecast Introduction................................................................................................................................. 3 Price Forecasts............................................................................................................................... 12 Oil Price Forecast Range

195

Energy conservation standards for new federal residential buildings: A decision analysis study using relative value discounting  

SciTech Connect

This report presents a reassessment of the proposed standard for energy conservation in new federal residential buildings. The analysis uses the data presented in the report, Economic Analysis: In Support of Interim Energy Conservation Standards for New Federal Residential Buildings (June 1988)-to be referred to as the EASIECS report. The reassessment differs from that report in several respects. In modeling factual information, it uses more recent forecasts of future energy prices and it uses data from the Bureau of the Census in order to estimate the distribution of lifetimes of residential buildings rather than assuming a hypothetical 25-year lifetime. In modeling social preferences decision analysis techniques are used in order to examine issues of public values that often are not included in traditional cost-benefit analyses. The present report concludes that the public would benefit from the proposed standard. Several issues of public values regarding energy use are illustrated with methods to include them in a formal analysis of a proposed energy policy. The first issue places a value on costs and benefits that will occur in the future as an irreversible consequence of current policy choices. This report discusses an alternative method, called relative value discounting which permits flexible discounting of future events-and the possibility of placing greater values on future events. The second issue places a value on the indirect benefits of energy savings so that benefits accrue to everyone rather than only to the person who saves the energy. This report includes non-zero estimates of the indirect benefits. The third issue is how the costs and benefits discussed in a public policy evaluation should be compared. In summary, selection of individual projects with larger benefit to cost ratios leads to a portfolio of projects with the maximum benefit to cost difference. 30 refs., 6 figs., 16 tabs. (JF)

Harvey, C. (Houston Univ., TX (USA). Coll. of Business Administration); Merkhofer, M.M.; Hamm, G.L. (Applied Decision Analysis, Inc., Menlo Park, CA (USA))

1990-07-02T23:59:59.000Z

196

Forecasts, Meteorology Services, Environmental Sciences Department  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecasts Short Term Forecast Suffolk County Northern Nassau Southern Nassau Area Forecast Discussion - OKX Area Forecast Discussion - NYS Area Forecast Discussion Mount Holly Area...

197

Austin Energy - Value of Solar Residential Rate (Texas) | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Austin Energy - Value of Solar Residential Rate (Texas) Austin Energy - Value of Solar Residential Rate (Texas) Austin Energy - Value of Solar Residential Rate (Texas) < Back Eligibility Residential Savings Category Solar Buying & Making Electricity Program Info Start Date 10/01/2012 State Texas Austin Energy, the municipal utility of Austin Texas, offers the Value of Solar rate for residential solar photovoltaic (PV) systems. The Value of Solar tariff, designed by Austin Energy and approved by Austin City Council in June 2012, will be available for all past, present and future residential solar customers beginning October 1, 2012. This tariff replaces net billing for residential solar PV systems no larger than 20 kilowatts (kW). Under this new tariff, residential customers will be credited monthly for their solar generation based on the Value of Solar energy generated from

198

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

Science Conference Proceedings (OSTI)

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

Not Available

1993-05-01T23:59:59.000Z

199

A short-range forecasting and inventory strategy for new product launches  

E-Print Network (OSTI)

Companies like Procter & Gamble that operate on a make-to-stock strategy use forecasts to drive their manufacturing, selling, and buying processes. Because forecasting future demand is not an exact science, inventory ...

Cheung, Christine

2005-01-01T23:59:59.000Z

200

Residential | OpenEI  

Open Energy Info (EERE)

Residential Residential 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 (7 months ago) Date Updated July 02nd, 2013 (5 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

Note: This page contains sample records for the topic "forecasts future residential" 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

A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication  

Science Conference Proceedings (OSTI)

In addition to structural measures, governmental authorities have set up flood forecasting systems to be used as early warning systems, to minimize the damage of future floods. These flood forecasting systems make use of hydrological and hydrodynamic ... Keywords: Non parametric approach, Operational flood forecasting, Probabilistic forecasting, Uncertainty estimation

N. Van Steenbergen; J. Ronsyn; P. Willems

2012-07-01T23:59:59.000Z

202

Verifying Forecasts Spatially  

Science Conference Proceedings (OSTI)

Numerous new methods have been proposed for using spatial information to better quantify and diagnose forecast performance when forecasts and observations are both available on the same grid. The majority of the new spatial verification methods can be ...

Eric Gilleland; David A. Ahijevych; Barbara G. Brown; Elizabeth E. Ebert

2010-10-01T23:59:59.000Z

203

Forecasting of Supercooled Clouds  

Science Conference Proceedings (OSTI)

Using parameterizations of cloud microphysics, a technique to forecast supercooled cloud events is suggested. This technique can be coupled on the mesoscale with a prognostic equation for cloud water to improve aircraft icing forecasts. The ...

André Tremblay; Anna Glazer; Wanda Szyrmer; George Isaac; Isztar Zawadzki

1995-07-01T23:59:59.000Z

204

Forecast Technical Document Volume Increment  

E-Print Network (OSTI)

Forecast Technical Document Volume Increment Forecasts A document describing how volume increment is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Volume increment forecasts Background A volume increment forecast is a fundamental output of the forecast

205

Time Series and Forecasting  

Science Conference Proceedings (OSTI)

Time Series and Forecasting. Leigh, Stefan and Perlman, S. (1991). "An Index for Comovement of Time Sequences With ...

206

Residential end-use energy planning system (REEPS). Final report  

Science Conference Proceedings (OSTI)

The Residential End-Use Energy Planning System (REEPS) is described. REEPS is a forecasting model of residential energy patterns that is capable of evaluating the impacts of a broad range of energy conservation measures. REEPS forecasts appliance installations, operating efficiencies, and utilization patterns for space heating, water heating, air conditioning, and cooking. Each of these decisions is sensitive to energy prices, mandatory policies, and household/dwelling and geographical characteristics. The parameters of these choice models have been estimated statistically from national household survey data. The structure of the choice models and the results of the statistical analysis are reported in detail. REEPS forecasts energy choices for a large number of market segments representing households with different socioeconomic, dwelling, and geographical characteristics. These segments reflect the joint distribution of characteristics in the population. Aggregate forecasts are generated by summing up the decisions for all population segments. This technique provides a consistent method of obtaining aggregate forecasts from disaggregate, nonlinear choice models. Moreover, it permits evaluation of the distributional impacts of prospective conservation policies. The results of simulation of REEPS are described. REEPS forecasts a moderate rise in electricity consumption per household and significant drops in other fuels. These are caused in part by high market penetrations of electric appliances which themselves reflect major shifts in relative energy prices.

Goett, A.; McFadden, D.

1982-07-01T23:59:59.000Z

207

Residential Wood Residential wood combustion (RWC) is  

E-Print Network (OSTI)

Residential Wood Combustion Residential wood combustion (RWC) is increasing in Europe because PM2.5. Furthermore, other combustion- related sources of OA in Europe may need to be reassessed. Will it affect global OA emission estimates? Combustion of biofuels is globally one of the major OA sources

208

Review of Residential Ventilation Technologies.  

NLE Websites -- All DOE Office Websites (Extended Search)

Review of Residential Ventilation Technologies. Review of Residential Ventilation Technologies. Title Review of Residential Ventilation Technologies. Publication Type Journal Article LBNL Report Number LBNL-57730 Year of Publication 2007 Authors Russell, Marion L., Max H. Sherman, and Armin F. Rudd Journal HVAC&R Research Volume 13 Start Page Chapter Pagination 325-348 Abstract This paper reviews current and potential ventilation technologies for residential buildings in North America and a few in Europe. The major technologies reviewed include a variety of mechanical systems, natural ventilation, and passive ventilation. Key parameters that are related to each system include operating costs, installation costs, ventilation rates, heat recovery potential. It also examines related issues such as infiltration, duct systems, filtration options, noise, and construction issues. This report describes a wide variety of systems currently on the market that can be used to meet ASHRAE Standard 62.2. While these systems generally fall into the categories of supply, exhaust or balanced, the specifics of each system are driven by concerns that extend beyond those in the standard and are discussed. Some of these systems go beyond the current standard by providing additional features (such as air distribution or pressurization control). The market will decide the immediate value of such features, but ASHRAE may wish to consider modifications to the standard in the future.

209

Nambe Pueblo Water Budget and Forecasting model.  

SciTech Connect

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

Brainard, James Robert

2009-10-01T23:59:59.000Z

210

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.

211

Dynamic Algorithm for Space Weather Forecasting System  

E-Print Network (OSTI)

We propose to develop a dynamic algorithm that intelligently analyzes existing solar weather data and constructs an increasingly more accurate equation/algorithm for predicting solar weather accurately in real time. This dynamic algorithm analyzes a wealth of data derived from scientific research and provides increasingly accurate solar forecasts. As the database of information grows over time, this algorithm perfects itself and reduces forecast uncertainties. This will provide a vastly more effective way of processing existing data for practical use in the public and private sectors. Specifically, we created an algorithm that stores data from several sources in a way that is useable, we created the ?dynamic algorithm? used for creating accurate/effective forecasts, and we have performed preliminary benchmarks on this algorithm. The preliminary benchmarks yield surprisingly effective results thus far?forecasts have been made 8-16 hours into the future with significant magnitude and trend accuracy, which is a vast improvement over current methods employed.

Fischer, Luke D.

2010-05-01T23:59:59.000Z

212

The Strategy of Professional Forecasting  

E-Print Network (OSTI)

This paper develops and compares two theories of strategic behavior of professional forecasters. The first theory posits that forecasters compete in a forecasting contest with pre-specified rules. In equilibrium of a winner-take-all contest, forecasts are excessively differentiated. According to the alternative reputational cheap talk theory, forecasters aim at convincing the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the realized state. If the market expects forecaster honesty, forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded.

Marco Ottaviani; Peter Norman Sørensen

2003-01-01T23:59:59.000Z

213

On Modeling and Forecasting Time Series of Smooth Curves  

E-Print Network (OSTI)

/fertility rate curves (Hyndman and Ullah, 2007; Erbas et al., 2007). Other examples include electricity system the rates are unobservable; hence one needs to forecast future rate profiles based on historical call of telephone customer service centers, where forecasts of daily call arrival rate profiles are needed

Shen, Haipeng

214

Residential demand response using reinforcement learning  

E-Print Network (OSTI)

Abstract — We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from 16 % to 40 % with respect to a price-unaware energy allocation. I.

Marco Levorato; Andrea Goldsmith; Urbashi Mitra

2010-01-01T23:59:59.000Z

215

Short-Term Wind Speed Forecasting for Power System Operations  

E-Print Network (OSTI)

Global large scale penetration of wind energy is accompanied by significant challenges due to the intermittent and unstable nature of wind. High quality short-term wind speed forecasting is critical to reliable and secure power system operations. This paper gives an overview of the current status of worldwide wind power developments and future trends, and reviews some statistical short-term wind speed forecasting models, including traditional time series models and advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented.

Xinxin Zhu; Marc G. Genton

2011-01-01T23:59:59.000Z

216

Burlington Electric Department - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Program Burlington Electric Department - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Appliances &...

217

Columbia Rural Electric Association - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Program Columbia Rural Electric Association - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Home...

218

Ozarks Electric Cooperative - Residential Energy Efficiency Loan...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Ozarks Electric Cooperative - Residential Energy Efficiency Loan Program Ozarks Electric Cooperative - Residential Energy Efficiency Loan Program Eligibility Residential Savings...

219

Kootenai Electric Cooperative - Residential Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Kootenai Electric Cooperative - Residential Efficiency Rebate Program Kootenai Electric Cooperative - Residential Efficiency Rebate Program Eligibility Residential Savings For Home...

220

Southwest Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Southwest Electric Cooperative - Residential Energy Efficiency Rebate Program Southwest Electric Cooperative - Residential Energy Efficiency Rebate Program Eligibility Residential...

Note: This page contains sample records for the topic "forecasts future residential" 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

Kirkwood Electric - Residential Energy Efficiency Rebate Program...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Kirkwood Electric - Residential Energy Efficiency Rebate Program Kirkwood Electric - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Heating &...

222

Central Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Programs Central Electric Cooperative - Residential Energy Efficiency Rebate Programs Eligibility Construction Residential Savings For Other...

223

Cherokee Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Cherokee Electric Cooperative - Residential Energy Efficiency Loan Programs Cherokee Electric Cooperative - Residential Energy Efficiency Loan Programs Eligibility Residential...

224

Marietta Power & Water - Residential Energy Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Marietta Power & Water - Residential Energy Efficiency Rebate Program Marietta Power & Water - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For...

225

SRP - Residential Energy Efficiency Rebate Program | Department...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

SRP - Residential Energy Efficiency Rebate Program SRP - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Home Weatherization Commercial...

226

Barron Electric Cooperative - Residential Energy Resource Conservation...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Resource Conservation Loan Program Barron Electric Cooperative - Residential Energy Resource Conservation Loan Program Eligibility Residential Savings For Home...

227

Cedar Falls Utilities - Residential Energy Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Program Cedar Falls Utilities - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Heating & Cooling Commercial...

228

TOPIC Brief BUILDING TECHNOLOGIES PROGRAM Lighting: Residential...  

NLE Websites -- All DOE Office Websites (Extended Search)

Lighting: Residential and Commercial Requirements TOPIC BRIEF 1 Lighting: Residential and Commercial Requirements Residential Lighting Requirements The 2009 International Energy...

229

Minnesota Valley Electric Cooperative -Residential Energy Resource...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Resource Conservation Loan Program Minnesota Valley Electric Cooperative -Residential Energy Resource Conservation Loan Program Eligibility Residential Savings...

230

Lake Region Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Region Electric Cooperative - Residential Energy Efficiency Rebate Program Lake Region Electric Cooperative - Residential Energy Efficiency Rebate Program Eligibility Residential...

231

ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL  

E-Print Network (OSTI)

5.5 ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL USING RETROSPECTIVE FORECASTS, Colorado 1. INTRODUCTION Improving weather forecasts is a primary goal of the U.S. National Oceanic predictions has been to improve the accuracy of the numerical forecast models. Much effort has been expended

Hamill, Tom

232

Large-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random Fields  

E-Print Network (OSTI)

pricing. Although it is known that probabilistic forecasts (which give a distribution over possible futureLarge-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random Fields Matt Wytock and J. Zico Kolter Abstract-- Short-term forecasting is a ubiquitous practice

Kolter, J. Zico

233

Residential/commercial market for energy technologies  

SciTech Connect

The residential/commercial market sector, particularly as it relates to energy technologies, is described. Buildings account for about 25% of the total energy consumed in the US. Market response to energy technologies is influenced by several considerations. Some considerations discussed are: industry characteristics; market sectors; energy-consumption characeristics; industry forecasts; and market influences. Market acceptance may be slow or nonexistent, the technology may have little impact on energy consumption, and redesign or modification may be necessary to overcome belatedly perceived market barriers. 7 figures, 20 tables.

Glesk, M.M.

1979-08-01T23:59:59.000Z

234

PPL Electric Utilities - Residential Energy Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Program PPL Electric Utilities - Residential Energy Efficiency Rebate Program Eligibility Multi-Family Residential Residential Savings For Home...

235

Distillate Fuel Oil Sales for Residential Use  

Annual Energy Outlook 2012 (EIA)

End Use Product: Residential - Distillate Fuel Oil Residential - No. 1 Residential - No. 2 Residential - Kerosene Commercial - Distillate Fuel Oil Commercial - No. 1 Distillate...

236

Atmos Energy (Gas) - Residential Efficiency Program | Department...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Atmos Energy (Gas) - Residential Efficiency Program Atmos Energy (Gas) - Residential Efficiency Program Eligibility Low-Income Residential Residential Savings For Heating & Cooling...

237

Benton PUD - Residential Energy Efficiency Rebate Programs |...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Programs Benton PUD - Residential Energy Efficiency Rebate Programs Eligibility Multi-Family Residential Residential Savings For Appliances &...

238

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network (OSTI)

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z

239

River Flow Forecasting for Reservoir management through Neural Networks  

Science Conference Proceedings (OSTI)

In utilities using a mixture of hydroelectric and nonhydroelectric power, the economics of the hydroelectric plants depend upon the reservoir height and the inflow into the reservoir for several months into the future. Accurate forecasts of reservoir ...

Meuser Valenca; Teresa Ludermir; Anelle Valenca

2005-12-01T23:59:59.000Z

240

A RELM earthquake forecast based on pattern informatics  

E-Print Network (OSTI)

We present a RELM forecast of future earthquakes in California that is primarily based on the pattern informatics (PI) method. This method identifies regions that have systematic fluctuations in seismicity, and it has been demonstrated to be successful. A PI forecast map originally published on 19 February 2002 for southern California successfully forecast the locations of sixteen of eighteen M>5 earthquakes during the past three years. The method has also been successfully applied to Japan and on a worldwide basis. An alternative approach to earthquake forecasting is the relative intensity (RI) method. The RI forecast map is based on recent levels of seismic activity of small earthquakes. Recent advances in the PI method show considerable improvement, particularly when compared with the RI method using relative operating characteristic (ROC) diagrams for binary forecasts. The RELM application requires a probability for each location for a number of magnitude bins over a five year period. We have therefore co...

Holliday, J R; Donnelan, A; Rundle, J B; Tiampo, K F; Turcotte, D L; Chen, Chien-chih; Donnelan, Andrea; Holliday, James R.; Rundle, John B.; Tiampo, Kristy F.; Turcotte, Donald L.

2005-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Building Technologies Office: Residential Buildings  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential Buildings Residential Buildings to someone by E-mail Share Building Technologies Office: Residential Buildings on Facebook Tweet about Building Technologies Office: Residential Buildings on Twitter Bookmark Building Technologies Office: Residential Buildings on Google Bookmark Building Technologies Office: Residential Buildings on Delicious Rank Building Technologies Office: Residential Buildings on Digg Find More places to share Building Technologies Office: Residential Buildings on AddThis.com... About Take Action to Save Energy Partner With DOE Activities Technology Research, Standards, & Codes Popular Residential Links Success Stories Previous Next Warming Up to Pump Heat. Lighten Energy Loads with System Design. Cut Refrigerator Energy Use to Save Money. Tools EnergyPlus Whole Building Simulation Program

242

Revised Draft Forecast of Electricity Demand  

E-Print Network (OSTI)

. Forecasts of higher electricity and natural gas prices will fundamentally challenge energy intensive. These include the reduced growth in natural gas supplies in spite of significant drilling activity and #12;DRAFT the medium-high case, while paper and allied products has been below the medium-low. Future natural gas

243

Neural-wavelet Methodology for Load Forecasting  

Science Conference Proceedings (OSTI)

Intelligent demand-side management represents a future trend of power system regulation. A key issue in intelligent demand-side management is accurate prediction of load within a local area grid (LAG), which is defined as a set of customers with an appropriate ... Keywords: load forecasting, load identification, neural-wavelet

Rong Gao; Lefteri H. Tsoukalas

2001-05-01T23:59:59.000Z

244

Probabilistic Forecasts from the National Digital Forecast Database  

Science Conference Proceedings (OSTI)

The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, ...

Roman Krzysztofowicz; W. Britt Evans

2008-04-01T23:59:59.000Z

245

ORNL integrated forecasting system  

SciTech Connect

This paper describes the integrated system for forecasting electric energy and load. In the system, service area models of electrical energy (kWh) and load distribution (minimum and maximum loads and load duration curve) are linked to a state-level model of electrical energy (kWh). Thus, the service area forecasts are conditional upon the state-level forecasts. Such a linkage reduces considerably the data requirements for modeling service area electricity demand.

Rizy, C.G.

1983-01-01T23:59:59.000Z

246

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

SciTech Connect

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 reference document provides a detailed description for energy analysts, other users, and the public. The NEMS Residential Sector Demand Module is currently used for mid-term forecasting purposes and energy policy analysis over the forecast horizon of 1993 through 2020. The model generates forecasts of energy demand for the residential sector by service, fuel, and Census Division. Policy impacts resulting from new technologies, market incentives, and regulatory changes can be estimated using the module. 26 refs., 6 figs., 5 tabs.

NONE

1998-01-01T23:59:59.000Z

247

forecast | OpenEI  

Open Energy Info (EERE)

Browse Upload data GDR Community Login | Sign Up Search Facebook icon Twitter icon forecast Dataset Summary Description The EIA's annual energy outlook (AEO) contains yearly...

248

Seasonal tropical cyclone forecasts  

E-Print Network (OSTI)

Seasonal forecasts of tropical cyclone activity in various regions have been developed since the first attempts in the early 1980s by Neville

Suzana J. Camargo; Anthony G. Barnston; Philip J. Klotzbach; Christopher W. Landsea

2007-01-01T23:59:59.000Z

249

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

250

Solar forecasting review  

E-Print Network (OSTI)

Online 24-h solar power forecasting based on weather typeweather observations at blue hill massachusetts,” Solarof weather patterns on the intensity of solar irradiance;

Inman, Richard Headen

2012-01-01T23:59:59.000Z

251

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,

252

OpenEI - Residential  

Open Energy Info (EERE)

Commercial and Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States http://en.openei.org/datasets/node/961 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/">Residential Energy Consumption Survey (RECS) for statistical references of building types

253

Residential Price - Marketers  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per Thousand Cubic Feet ...

254

Essays on residential desegregation  

E-Print Network (OSTI)

Many ethnically diverse countries have policies that encourage integration across ethnic groups. This dissertation investigates the impact and welfare implications of a residential desegregation policy in Singapore, the ...

Wong, Maisy

2008-01-01T23:59:59.000Z

255

Choosing a Residential Window  

NLE Websites -- All DOE Office Websites (Extended Search)

Choosing a Residential Window LBNLs Windows and Daylighting Group provides technical support to government and industry efforts to help consumers and builders choose...

256

Global and Local Skill Forecasts  

Science Conference Proceedings (OSTI)

A skill forecast gives the probability distribution for the error in a forecast. Statistically, Well-founded skill forecasting methods have so far only been applied within the context of simple models. In this paper, the growth of analysis errors ...

P. L. Houtekamer

1993-06-01T23:59:59.000Z

257

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network (OSTI)

Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN; the former with primary contributions in the areas of climate and hydrologic forecasting and the latter Service (NWS) California Nevada River Forecast Center (CNRFC), the California Department of Water

258

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network (OSTI)

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

259

Does the term structure forecast  

E-Print Network (OSTI)

provides more accurate forecasts of real consumption growth14. Harvey, C.R. (1989): \\Forecasts of economic growth fromC.R. (1993): \\Term structure forecasts economic growth", Fi-

Berardi, Andrea; Torous, Walter

2002-01-01T23:59:59.000Z

260

Distortion Representation of Forecast Errors  

Science Conference Proceedings (OSTI)

Forecast error is decomposed into three components, termed displacement error, amplitude error, mid residual error, respectively. Displacement error measures how much of the forecast error can be accounted for by moving the forecast to best fit ...

Ross N. Hoffman; Zheng Liu; Jean-Francois Louis; Christopher Grassoti

1995-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Composite forecasting in commodity systems  

E-Print Network (OSTI)

Paper No. COMPOSI1E FORECASTING IN CO/Yt.flDITI SYSTfu\\1S1980 .i CfIAPTER COMPOSITE FORECASTING IN COMMOOITY SYSTEMS*to utilizeeconometric .modelsfor forecasting ! ,urposes. The

Johnson, Stanley R; Rausser, Gordon C.

1980-01-01T23:59:59.000Z

262

Light truck forecasts  

SciTech Connect

The recent dramatic increase in the number of light trucks (109% between 1963 and 1974) has prompted concern about the energy consequences of the growing popularity of the light truck. An estimate of the future number of light trucks is considered to be a reasonable first step in assessing the energy impact of these vehicles. The monograph contains forecasts based on two models and six scenarios. The coefficients for the models have been derived by ordinary least squares regression of national level time series data. The first model is a two stage model. The first stage estimates the number of light trucks and cars (together), and the second stage applies a share's submodel to determine the number of light trucks. The second model is a simultaneous equation model. The two models track one another remarkably well, within about 2%. The scenarios were chosen to be consistent with those used in the Lindsey-Kaufman study Projection of Light Truck Population to Year 2025. Except in the case of the most dismal economic scenario, the number of light trucks is expected to increase from the 1974 level of 0.09 light truck per person to about 0.12 light truck per person in 1995.

Liepins, G.E.

1979-09-01T23:59:59.000Z

263

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 operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model.

Zhu, Xinxin

2013-08-01T23:59:59.000Z

264

Coefficients for Debiasing Forecasts  

Science Conference Proceedings (OSTI)

Skill-score decompositions can be used to analyze the effects of bias on forecasting skill. However, since bias terms are typically squared, and bias is measured in skill-score units rather than in units of the forecasts, such decompositions only ...

Thomas R. Stewart; Patricia Reagan-Cirincione

1991-08-01T23:59:59.000Z

265

Evaluating Point Forecasts  

E-Print Network (OSTI)

Typically, point forecasting methods are compared and assessed by means of an error measure or scoring function, such as the absolute error or the squared error. The individual scores are then averaged over forecast cases, to result in a summary measure of the predictive performance, such as the mean absolute error or the (root) mean squared error. I demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are carefully matched. Effective point forecasting requires that the scoring function be specified ex ante, or that the forecaster receives a directive in the form of a statistical functional, such as the mean or a quantile of the predictive distribution. If the scoring function is specified ex ante, the forecaster can issue the optimal point forecast, namely, the Bayes rule. If the forecaster receives a directive in the form of a functional, it is critical that the scoring function be consistent for it, in the sense that the expect...

Gneiting, Tilmann

2009-01-01T23:59:59.000Z

266

Forecasters ’ Objectives and Strategies ?  

E-Print Network (OSTI)

This chapter develops a unified modeling framework for analyzing the strategic behavior of forecasters. The theoretical model encompasses reputational objectives, competition for the best accuracy, and bias. Also drawing from the extensive literature on analysts, we review the empirical evidence on strategic forecasting and illustrate how our model can be structurally estimated.

Iván Marinovic; Marco Ottaviani; Peter Norman Sørensen

2011-01-01T23:59:59.000Z

267

Fuel consumption: Industrial, residential, and general studies. (Latest citations from the NTIS Bibliographic database). Published Search  

SciTech Connect

The bibliography contains citations concerning fuel consumption in industrial and residential sectors. General studies of fuel supply, demand, policy, forecasts, and consumption models are presented. Citations examine fuel information and forecasting systems, fuel production, international economic and energy activities, heating oils, and pollution control. Fuel consumption in the transportation sector is covered in a separate bibliography. (Contains 250 citations and includes a subject term index and title list.)

Not Available

1994-08-01T23:59:59.000Z

268

Edmund G. Brown Jr. LIGHTING CALIFORNIA'S FUTURE  

E-Print Network (OSTI)

Edmund G. Brown Jr. Governor LIGHTING CALIFORNIA'S FUTURE: SMART LIGHT-EMITTING DIODE LIGHTING's Future: Smart LightEmitting Diode Lighting in Residential Fans. California Energy Commission, PIER. For the Smart Light emitting Diode Lighting in Residential Fans Project, the California Lighting Technology

269

Long-term residential load forecasting. Final report  

SciTech Connect

The main objective of this study was to isolate and evaluate the importance of various factors, many of which are household characteristics and weather conditions, that determine the demand for electricity at different times of day. A second purpose was to investigate one of the factors in detail, namely, prices, which was feasible because half of the households in the sample were subjected to time-of-day pricing. Substantial differences between the load curves of the experimental and control groups were found. Households in the experimental group significantly decreased electricity usage when its price was high, the consumption being shifted partly into the early morning hours but more heavily into the evening. The importance of certain appliances in shifting the load curve is also clearly brought out. For example, households with a dishwasher or electric heating appeared to change the timing of use of these appliances under peak-load pricing. Other appliances were also important in determining the load curve for both groups. Swimming pool pumps and air conditioning, for instance, were important determinants in the summer, whereas in the winter, electric heating and dishwashers substantially increased consumption levels.

Granger, C.W.J.; Engle, R.F.; Ramanathan, R.; Andersen, A.

1978-02-01T23:59:59.000Z

270

A New Verification Score for Public Forecasts  

Science Conference Proceedings (OSTI)

CREF, a new verification score for public forecasts, is introduced. This verification score rewards a forecaster who forecasts a rare event accurately. CREF was used to verify local forecasts at the Weather Service Forecast Office (WSFO) in ...

Dean P. Gulezian

1981-02-01T23:59:59.000Z

271

RESIDENTIAL ENERGY CONSUMPTION SURVEY 1997 CONSUMPTION AND ...  

U.S. Energy Information Administration (EIA)

Residential Sector energy Intensities for 1978-1997 using data from EIA Residential Energy Consumption Survey.

272

Residential Humidity Control Strategies  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Humidity Control Strategies Residential Humidity Control Strategies Armin Rudd Residential Energy Efficiency Stakeholder Meeting 2/29 - 3/2/2012 Austin, Texas 2 Residential Energy Efficiency Stakeholder Meeting 2/29 - 3/2/2012 Austin, Texas Humidity control goals  Comfort, and Indoor Air Quality  Control indoor humidity year-around, just like we do temperature  Durability and customer satisfaction  Reduce builder risk and warranty/service costs 2 3 Residential Energy Efficiency Stakeholder Meeting 2/29 - 3/2/2012 Austin, Texas Humidity control challenges 1. In humid cooling climates, there will always be times of the year when there is little sensible cooling load to create thermostat demand but humidity remains high * Cooling systems that modify fan speed and temperature set point based on humidity can help but are still limited

273

Future risks of satellite-based tracking  

Science Conference Proceedings (OSTI)

This study finds out if in the future, some special risks concerning satellite-based tracking and navigation occur. To find out possible future risks, future research methods such as scenarios were being used. Forecasting the future is impossible, but ... Keywords: future research, risk management, satellite-base tracking, satellite-based navigation, tracking

Miikka Ohisalo; Otto Tiuri; Tatu Urpila; Jyri Rajamäki

2011-03-01T23:59:59.000Z

274

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network (OSTI)

· NATIONAL AND GLOBAL FORECASTS · WEST VIRGINIA PROFILES AND FORECASTS · ENERGY · HEALTHCARE Industry Insight: West Virginia Fiscal Forecast 34 CHAPTER 4: WEST ViRGiNiA'S 35 COUNTiES AND MSAs West Forecast Summary 2 CHAPTER 1: THE UNiTED STATES ECONOMY Figure 1.1: United States Real GDP Growth 3 Figure

Mohaghegh, Shahab

275

APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY  

E-Print Network (OSTI)

1 APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY Rick Katz.isse.ucar.edu/HP_rick/dmuu.pdf #12;2 QUOTES ON USE OF PROBABILITY FORECASTS · Lao Tzu (Chinese Philosopher) "He who knows does and Value of Probability Forecasts (4) Cost-Loss Decision-Making Model (5) Simulation Example (6) Economic

Katz, Richard

276

Residential Retrofit Program Design Guide  

NLE Websites -- All DOE Office Websites (Extended Search)

Technical Assistance Program Technical Assistance Program Residential Retrofit Program Design Guide May 2011 This work has been performed by the Vermont Energy Investment Corporation (VEIC) and Energy Futures Group (EFG), under the Contract No. 4200000341 with Oak Ridge National Laboratory which is managed by UT-Battelle, LLC under Contract with the US Department of Energy No. DE-AC05-00OR22725. This document was prepared in collaboration with a partnership of companies under this contract. The partnership is led by the Vermont Energy Investment Corporation (VEIC), and includes the following companies: American Council for an Energy Efficient Economy (ACEEE), Energy Futures Group (EFG), Midwest Energy Efficiency Alliance (MEEA), Northwest Energy Efficiency Alliance (NEEA), Northeast Energy Efficiency Partnership (NEEP), Natural

277

Why are survey forecasts superior to model forecasts?  

E-Print Network (OSTI)

We investigate two characteristics of survey forecasts that are shown to contribute to their superiority over purely model-based forecasts. These are that the consensus forecasts incorporate the effects of perceived changes in the long-run outlook, as well as embodying departures from the path toward the long-run expectation. Both characteristics on average tend to enhance forecast accuracy. At the level of the individual forecasts, there is scant evidence that the second characteristic enhances forecast accuracy, and the average accuracy of the individual forecasts can be improved by applying a mechanical correction.

Michael P. Clements; Michael P. Clements

2010-01-01T23:59:59.000Z

278

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network (OSTI)

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

279

LOAD FORECASTING Eugene A. Feinberg  

E-Print Network (OSTI)

's electricity price forecasting model, produces forecast of gas demand consistent with electric load. #12Gas demand Council's Market Price of Electricity Forecast Natural GasDemand Electric Load Aggregating Natural between the natural gas and electricity and new uses of natural gas emerge. T natural gas forecasts

Feinberg, Eugene A.

280

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

DOE Green Energy (OSTI)

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.

2012-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Factors Driving Prices & Forecast  

Gasoline and Diesel Fuel Update (EIA)

This spread is a function of the balance between demand and fresh supply (production and net imports). Finally I will discuss the current forecast for distillate prices this winter...

282

Modeling and Forecasting Aurora  

Science Conference Proceedings (OSTI)

Modeling the physical processes needed for forecasting space-weather events requires multiscale modeling. This article discusses several modelsresearchers use to treat the various auroral processes that influence space weather.

Dirk Lummerzheim

2007-01-01T23:59:59.000Z

283

Valuing Climate Forecast Information  

Science Conference Proceedings (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

284

Residential | Open Energy Information  

Open Energy Info (EERE)

Residential Residential Jump to: navigation, search Click to return to AEO2011 page AEO2011 Data From AEO2011 report . Market Trends In the AEO2011 Reference case, residential energy use per capita declines by 17.0 percent from 2009 to 2035 (Figure 58). Delivered energy use stays relatively constant while population grows by 26.7 percent during the period. Growth in the number of homes and in average square footage leads to increased demand for energy services, which is offset in part by efficiency gains in space heating, water heating, and lighting equipment. Population shifts to warmer and drier climates also reduce energy demand for space heating.[1] Issues in Focus In 2009, the residential and commercial buildings sectors used 19.6 quadrillion Btu of delivered energy, or 21 percent of total U.S. energy

285

Residential Energy Disclosure (Hawaii)  

Energy.gov (U.S. Department of Energy (DOE))

A residential property owner is required to disclose electricity costs for the most recent three-month period in which the property was occupied as a condition of selling it. No proof or copies of...

286

Residential propane prices increase  

Gasoline and Diesel Fuel Update (EIA)

from last week to 2.62 per gallon; up 37.4 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. The retail price...

287

Residential propane prices increase  

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

from last week to 2.57 per gallon; up 32.2 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. The retail price...

288

Residential propane prices increase  

Gasoline and Diesel Fuel Update (EIA)

a week ago to 2.76 per gallon. That's up 51.2 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. Propane prices...

289

Residential propane prices increase  

Gasoline and Diesel Fuel Update (EIA)

a week ago to 2.71 per gallon. That's up 46.9 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. Propane prices...

290

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - 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.

Owen Comstock

2012-12-19T23:59:59.000Z

291

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - 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.

Owen Comstock

2013-11-05T23:59:59.000Z

292

Residential Gateways and Controllers  

Science Conference Proceedings (OSTI)

Energy companies are exploring two-way residential communications to help reduce the cost of providing standard energy-related services, such as itemized billing or demand reduction, as well as to provide nontraditional services, such as diagnostic services and e-mail. This report covers the key to development of these services -- residential gateways and controllers. The report was prepared with both technical and financial energy company managers in mind, for use as a reference tool and strategic plann...

1999-08-31T23:59:59.000Z

293

Density Forecasting for Long-Term Peak Electricity Demand  

E-Print Network (OSTI)

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007–2008.

Rob J. Hyndman; Shu Fan

2009-01-01T23:59:59.000Z

294

Multivariate Forecast Evaluation And Rationality Testing  

E-Print Network (OSTI)

1062—1088. MULTIVARIATE FORECASTS Chaudhuri, P. (1996): “OnKingdom. MULTIVARIATE FORECASTS Kirchgässner, G. , and U. K.2005): “Estimation and Testing of Forecast Rationality under

Komunjer, Ivana; OWYANG, MICHAEL

2007-01-01T23:59:59.000Z

295

Forecasting in the Presence of Level Shifts  

E-Print Network (OSTI)

accuracy. Journal of Forecasting 19 : 537-560. Hamilton JD.430. Harvey AC. 1989. Forecasting, structural time seriesMH, Timmermann A. 1994. Forecasting stock returns: An

Smith, Aaron

2004-01-01T23:59:59.000Z

296

Forecasting the Impact of the Irregular Events with DIPA Methodology  

Science Conference Proceedings (OSTI)

The impact of the irregular events on oil futures markets is so great that it is even superior to the variation tendency of the time series itself. In order to forecast the influence of the irregular events on oil futures price, based on the thorough ...

Jinrong Zhu

2008-01-01T23:59:59.000Z

297

Forecasting technology costs via the Learning Curve - Myth or Magic?  

E-Print Network (OSTI)

is generally considered to be traditional fossil fuel power stations, hence making a further assumption that such a value for cost can be forecasted). In situations where niche markets exist (for example solar PV electricity for remote areas or hand held... Solar PV provides a good example of the use and dangers of using experience curves to forecast future costs of an energy technology. It is a good example since solar PV modules are generally accessed by an international market allowing for worldwide...

Alberth, Stephan

298

Reading Municipal Light Department - Residential ENERGY STAR...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential ENERGY STAR Appliance Rebate Program Reading Municipal Light Department - Residential ENERGY STAR Appliance Rebate Program Eligibility Residential Savings For Heating &...

299

Chicopee Electric Light - Residential Solar Rebate Program |...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Chicopee Electric Light - Residential Solar Rebate Program Chicopee Electric Light - Residential Solar Rebate Program Eligibility Residential Savings For Solar Buying & Making...

300

Lane Electric Cooperative - Residential Energy Efficiency Loan...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Energy Efficiency Loan Programs Lane Electric Cooperative - Residential Energy Efficiency Loan Programs Eligibility Multi-Family Residential Residential Savings For Home...

Note: This page contains sample records for the topic "forecasts future residential" 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

Membership Criteria: Better Buildings Residential network  

NLE Websites -- All DOE Office Websites (Extended Search)

Criteria BETTER BUILDINGS RESIDENTIAL NETWORK Learn more at betterbuildings.energy.govbbrn Better Buildings Residential Network (BBRN) members must be supportive of residential...

302

Residential Mobility and Latino Political Mobilization  

E-Print Network (OSTI)

Brians, Craig Leonard. 1997. “Residential Mobility, VoterHighton, Benjamin. 2000. "Residential Mobility, Community2003. “ Language Choice, Residential Stability and Voting

Ramirez, Ricardo

2005-01-01T23:59:59.000Z

303

RESIDENTIAL THERMOSTATS: COMFORT CONTROLS IN CALIFORNIA HOMES  

E-Print Network (OSTI)

Report on Applicability of Residential Ventilation StandardsCharacterization of Residential New Construction PracticesJ - Load Calculation for Residential Winter and Summer Air

Meier, Alan K.

2008-01-01T23:59:59.000Z

304

Evaluation of evolving residential electricity tariffs  

E-Print Network (OSTI)

Evaluation of evolving residential electricity tariffs JudyEvaluation of evolving residential electricity tariffs Judyjdonadee@andrew.cmu.edu Abstract Residential customers in

Lai, Judy

2011-01-01T23:59:59.000Z

305

Landholders, Residential Land Conversion, and Market Signals  

E-Print Network (OSTI)

465– Margulis: Landholders, Residential Land Conversion, and1983. An Analysis of Residential Developer Location FactorsHow Regulation Affects New Residential Development. New

Margulis, Harry L.

2006-01-01T23:59:59.000Z

306

Infiltration in ASHRAE's Residential Ventilation Standards  

E-Print Network (OSTI)

Related  to  Residential  Ventilation  Requirements”.  Rudd,  A.   2005.   “Review  of  Residential  Ventilation and  Matson  N.E. ,  “Residential  Ventilation  and  Energy 

Sherman, Max

2008-01-01T23:59:59.000Z

307

Forecasting Cosmological Constraints from Redshift Surveys  

E-Print Network (OSTI)

Observations of redshift-space distortions in spectroscopic galaxy surveys offer an attractive method for observing the build-up of cosmological structure, which depends both on the expansion rate of the Universe and our theory of gravity. In this paper we present a formalism for forecasting the constraints on the growth of structure which would arise in an idealized survey. This Fisher matrix based formalism can be used to study the power and aid in the design of future surveys.

Martin White; Yong-Seon Song; Will J. Percival

2008-10-08T23:59:59.000Z

308

Residential and Commercial Briefings 2000: Characteristics of the Retail Marketplace  

Science Conference Proceedings (OSTI)

This industry report examines changes in the competitive electricity market throughout the year 2000, and how these changes affect residential and commercial customers. The following issues are discussed: o Characteristics of the residential and commercial markets: current and future energy use data by market and fuel type o Industry restructuring, deregulation, and its energy suppliers: deregulation issues by state and energy supplier activity within those states o Corporate moves, mergers, and business...

2002-02-07T23:59:59.000Z

309

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect

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

310

Decision support for financial forecasting  

SciTech Connect

A primary mission of the Budget Management Division of the Air Force is fiscal analysis. This involves formulating, justifying, and tracking financial data during budget preparation and execution. An essential requirement of this process is the ready availability and easy manipulation of past and current budget data. This necessitates the decentralization of the data. A prototypical system, BAFS (Budget Analysis and Forecasting System), that provides such a capability is presented. In its current state, the system is designed to be a decision support tool. A brief report of the budget decisions and activities is presented. The system structure and its major components are discussed. An insight into the implementation strategies and the tool used is provided. The paper concludes with a discussion of future enhancements and the system's evolution into an expert system. 4 refs., 3 figs.

Jairam, B.N.; Morris, J.D.; Emrich, M.L.; Hardee, H.K.

1988-10-01T23:59:59.000Z

311

FROM ANALYSTS ' EARNINGS FORECASTS  

E-Print Network (OSTI)

We examine the accuracy and bias of intrinsic equity prices estimated from three accounting-based valuation models using analyst’s earnings forecasts over a four-year horizon. The models are: (a) the earnings capitalization model, (b) the residual income model without a terminal value, and (c) the residual income model with a terminal value that assumes residual income will grow beyond the horizon at a constant rate determined from the expected residual income growth rate over the forecast horizon. Our analysis is based on valuation errors that are calculated by comparing estimated prices to actual prices. We contribute to the literature by examining whether: (i) the analysts ’ earnings forecasts convey information about value beyond that conveyed by current earnings, book value and dividends, (ii) the use of firm specific growth rates in terminal value calculations results in more unbiased and accurate valuations than the use of constant growth rates, and (iii) different models perform better under different ex-ante conditions. We find that analysts ’ earnings forecasts convey information about value beyond that conveyed by current earnings, book values and dividends. Each of the models that we used has valuation errors that decline monotonically as the horizon increases implying that earnings forecasts at each horizon convey new value relevant information. We cannot find a clear advantage to using firm specific growth rates instead of a constant rate of 4 % across all sample

Theodore Sougiannis; Takashi Yaekura

2000-01-01T23:59:59.000Z

312

About Residential | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Buildings » About Residential Residential Buildings » About Residential About Residential The Building Technologies Office (BTO) collaborates with home builders, energy professionals, state and local governments, utilities, product manufacturers, educators, and researchers to improve the energy efficiency of both new and existing homes. Residential Sector Activities Include: Demonstrating to builders and remodelers how to build and renovate for high performance through best practice guides and case studies and continuing to developing innovative whole-house energy efficiency solutions through Building America research projects. We also provide guidelines and tools for researchers conducting building related research projects. Promoting a trusted, whole-house process for upgrading existing homes with

313

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast

314

Jasper County REMC - Residential Residential Energy Efficiency Rebate  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Jasper County REMC - Residential Residential Energy Efficiency Jasper County REMC - Residential Residential Energy Efficiency Rebate Program Jasper County REMC - Residential Residential Energy Efficiency Rebate Program < Back Eligibility Residential Savings Category Heating & Cooling Commercial Heating & Cooling Heat Pumps Appliances & Electronics Water Heating Program Info State Indiana Program Type Utility Rebate Program Rebate Amount Refrigerator Recycling: $35 Heat Pump Water Heater: $400 Air-Source Heat Pumps: $250 - $1,500/unit (Power Moves rebate), $200 (REMC Bill Credit) Dual Fuel Heat Pumps: $1,500/unit Geothermal Heat Pumps: $1,500/unit (Power Moves rebate), $500 (REMC Bill Credit) Provider Jasper County REMC Jasper County REMC, in conjunction with Wabash Valley Power Association's Power Moves programs, offers a range of rebates to its residential

315

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts The International Energy Outlook is prepared by the Energy Information Administration (EIA). General questions concerning the contents of the report should be referred to John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Office of Integrated Analysis and Forecasting. Specific questions about the report should be referred to Linda E. Doman (202/586-1041) or the following analysts: World Energy and Economic Outlook Linda Doman (linda.doman@eia.doe.gov, 202-586-1041) Macroeconomic Assumptions Nasir Khilji (nasir.khilji@eia.doe.gov, 202-586-1294) Energy Consumption by End-Use Sector Residential Energy Use John Cymbalsky (john.cymbalsky@eia.doe.gov, 202-586-4815) Commercial Energy Use Erin Boedecker (erin.boedecker@eia.doe.gov, 202-586-4791)

316

Residential Building Code Compliance  

NLE Websites -- All DOE Office Websites (Extended Search)

6 6 Residential Building Code Compliance: Recent Findings and Implications Energy use in residential buildings in the U.S. is significant-about 20% of primary energy use. While several approaches reduce energy use such as appliance standards and utility programs, enforcing state building energy codes is one of the most promising. However, one of the challenges is to understand the rate of compliance within the building community. Utility companies typically use these codes as the baseline for providing incentives to builders participating in utility-sponsored residential new construction (RNC) programs. However, because builders may construct homes that fail to meet energy codes, energy use in the actual baseline is higher than would be expected if all buildings complied with the code. Also,

317

Progress in Residential Retrofit  

NLE Websites -- All DOE Office Websites (Extended Search)

The Cutting Edge: Progress in Residential Retrofit The Cutting Edge: Progress in Residential Retrofit A geographic representation of saturations of ceiling fans based on data from the RASSes. White areas indicate a lack of data for that region. Many utilities survey their customers to learn more about the buildings and the occupants in their service areas. These surveys-usually called "residential appliance saturation surveys," or RASSes-ask for the number and types of appliances present, the number of people living in the home, and sometimes personal information. The RASSes are also used to collect information about the presence of conservation measures such as wall and ceiling insulation, weatherstripping, multipane windows, and water flow restrictors. Building Energy Analysis Group researchers Alan Meier and Brian Pon gathered RASSes

318

Building Technologies Residential Survey  

SciTech Connect

Introduction A telephone survey of 1,025 residential occupants was administered in late October for the Building Technologies Program (BT) to gather information on residential occupant attitudes, behaviors, knowledge, and perceptions. The next section, Survey Results, provides an overview of the responses, with major implications and caveats. Additional information is provided in three appendices as follows: - Appendix A -- Summary Response: Provides summary tabular data for the 13 questions that, with subparts, comprise a total of 25 questions. - Appendix B -- Benchmark Data: Provides a benchmark by six categories to the 2001 Residential Energy Consumption Survey administered by EIA. These were ownership, heating fuel, geographic location, race, household size and income. - Appendix C -- Background on Survey Method: Provides the reader with an understanding of the survey process and interpretation of the results.

Secrest, Thomas J.

2005-11-07T23:59:59.000Z

319

Average Residential Price  

Gasoline and Diesel Fuel Update (EIA)

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

320

Residential Buildings Integration Program  

NLE Websites -- All DOE Office Websites (Extended Search)

David Lee David Lee Program Manager David.Lee@ee.doe.gov 202-287-1785 April 2, 2013 Residential Buildings Integration Program Building Technologies Office Program Peer Review 2 | Building Technologies Office eere.energy.gov Sub-Programs for Review Better Buildings Neighborhood Program Building America Challenge Home Home Energy Score Home Performance with ENERGY STAR Solar Decathlon 3 | Building Technologies Office eere.energy.gov How Residential Buildings Fits into BTO Research & Development * Develop technology roadmaps * Prioritize opportunities * Solicit and select innovative technology solutions * Collaborate with researchers

Note: This page contains sample records for the topic "forecasts future residential" 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

Average Residential Price  

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

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

322

Residential Buildings Integration Program  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

David Lee David Lee Program Manager David.Lee@ee.doe.gov 202-287-1785 April 2, 2013 Residential Buildings Integration Program Building Technologies Office Program Peer Review 2 | Building Technologies Office eere.energy.gov Sub-Programs for Review Better Buildings Neighborhood Program Building America Challenge Home Home Energy Score Home Performance with ENERGY STAR Solar Decathlon 3 | Building Technologies Office eere.energy.gov How Residential Buildings Fits into BTO Research & Development * Develop technology roadmaps * Prioritize opportunities * Solicit and select innovative technology solutions * Collaborate with researchers

323

Measuring Residential Ventilation  

NLE Websites -- All DOE Office Websites (Extended Search)

Measuring Residential Ventilation Measuring Residential Ventilation System Airflows: Part 2 - Field Evaluation of Airflow Meter Devices and System Flow Verification J. Chris Stratton, Iain S. Walker, Craig P. Wray Environmental Energy Technologies Division October 2012 LBNL-5982E 2 Disclaimer This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any

324

Firelands Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Firelands Electric Cooperative - Residential Energy Efficiency Rebate Program Firelands Electric Cooperative - Residential Energy Efficiency Rebate Program < Back Eligibility...

325

South Alabama Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

South Alabama Electric Cooperative - Residential Energy Efficiency Loan Program South Alabama Electric Cooperative - Residential Energy Efficiency Loan Program Eligibility...

326

Central Alabama Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Central Alabama Electric Cooperative - Residential Energy Efficiency Rebate Program Central Alabama Electric Cooperative - Residential Energy Efficiency Rebate Program Eligibility...

327

Cookeville Electric Department - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Cookeville Electric Department - Residential Energy Efficiency Rebate Program Cookeville Electric Department - Residential Energy Efficiency Rebate Program Eligibility Commercial...

328

Lane Electric Cooperative - Residential and Commercial Weatherization...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

and Commercial Weatherization Grant Program Lane Electric Cooperative - Residential and Commercial Weatherization Grant Program Eligibility Commercial Low-Income Residential...

329

Lane Electric Cooperative - Residential Efficiency Rebate Program...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Efficiency Rebate Program Lane Electric Cooperative - Residential Efficiency Rebate Program Eligibility Residential Savings For Appliances & Electronics Home Weatherization...

330

Austin Energy - Residential Energy Efficiency Rebate Program...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Rebate Program Austin Energy - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Home Weatherization Commercial Weatherization Heating & Cooling...

331

Meeting Residential Ventilation Standards Through Dynamic Control...  

NLE Websites -- All DOE Office Websites (Extended Search)

Meeting Residential Ventilation Standards Through Dynamic Control of Ventilation Systems Title Meeting Residential Ventilation Standards Through Dynamic Control of Ventilation...

332

Maximizing Information from Residential Measurements of Volatile...  

NLE Websites -- All DOE Office Websites (Extended Search)

Maximizing Information from Residential Measurements of Volatile Organic Compounds Title Maximizing Information from Residential Measurements of Volatile Organic Compounds...

333

American Municipal Power (Public Electric Utilities) - Residential...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

American Municipal Power (Public Electric Utilities) - Residential Efficiency Smart Program (Ohio) American Municipal Power (Public Electric Utilities) - Residential Efficiency...

334

Southern Pine Electric Power Association - Residential Energy...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Southern Pine Electric Power Association - Residential Energy Efficiency Rebate Program Southern Pine Electric Power Association - Residential Energy Efficiency Rebate Program <...

335

Energy Smart - Residential Energy Efficiency Rebate Program ...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Smart - Residential Energy Efficiency Rebate Program (20 Municipalities) Energy Smart - Residential Energy Efficiency Rebate Program (20 Municipalities) < Back Eligibility...

336

Ozark Border Electric Cooperative - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Ozark Border Electric Cooperative - Residential Energy Efficiency Rebate Program Ozark Border Electric Cooperative - Residential Energy Efficiency Rebate Program Eligibility...

337

Central New Mexico Electric Cooperative - Residential Energy...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

New Mexico Electric Cooperative - Residential Energy Efficiency Rebate Program Central New Mexico Electric Cooperative - Residential Energy Efficiency Rebate Program Eligibility...

338

Essays in International Macroeconomics and Forecasting  

E-Print Network (OSTI)

This dissertation contains three essays in international macroeconomics and financial time series forecasting. In the first essay, I show, numerically, that a two-country New-Keynesian Sticky Prices model, driven by monetary and productivity shocks, is capable of explaining the highly positive correlation across the industrialized countries' inflation even though their cross-country correlation in money growth rate is negligible. The structure of this model generates cross-country correlations of inflation, output and consumption that appear to closely correspond to the data. Additionally, this model can explain the internal correlation between inflation and output observed in the data. The second essay presents two important results. First, gains from monetary policy cooperation are different from zero when the elasticity of substitution between domestic and imported goods consumption is different from one. Second, when monetary policy is endogenous in a two-country model, the only Nash equilibria supported by this model are those that are symmetrical. That is, all exporting firms in both countries choose to price in their own currency, or all exporting firms in both countries choose to price in the importer's currency. The last essay provides both conditional and unconditional predictive ability evaluations of the aluminum futures contracts prices, by using five different econometric models, in forecasting the aluminum spot price monthly return 3, 15, and 27-months ahead for the sample period 1989.01-2010.10. From these evaluations, the best model in forecasting the aluminum spot price monthly return 3 and 15 months ahead is followed by a (VAR) model whose variables are aluminum futures contracts price, aluminum spot price and risk free interest rate, whereas for the aluminum spot price monthly return 27 months ahead is a single equation model in which the aluminum spot price today is explained by the aluminum futures price 27 months earlier. Finally, it shows that iterated multiperiod-ahead time series forecasts have a better conditional out-of-sample forecasting performance of the aluminum spot price monthly return when an estimated (VAR) model is used as a forecasting tool.

Bejarano Rojas, Jesus Antonio

2011-08-01T23:59:59.000Z

339

Consensus Coal Production Forecast for  

E-Print Network (OSTI)

Consensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks References 27 #12;W.Va. Consensus Coal Forecast Update 2009 iii List of Tables 1. W.Va. Coal Production

Mohaghegh, Shahab

340

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

Note: This page contains sample records for the topic "forecasts future residential" 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

Forecast Technical Document Technical Glossary  

E-Print Network (OSTI)

Forecast Technical Document Technical Glossary A document defining some of the terms used in the 2011 Production Forecast technical documentation. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the Forecast documentation. In some cases, the terms and the descriptions are "industry standard", in others

342

Forecast Technical Document Tree Species  

E-Print Network (OSTI)

Forecast Technical Document Tree Species A document listing the tree species included in the 2011 Production Forecast Tom Jenkins Justin Gilbert Ewan Mackie Robert Matthews #12;PF2011 ­ List of tree species The following is the list of species used within the Forecast System. Species are ordered alphabetically

343

3, 21452173, 2006 Probabilistic forecast  

E-Print Network (OSTI)

HESSD 3, 2145­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page for probabilistic forecasts of continuous hydrological variables F. Laio and S. Tamea DITIC ­ Department­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page Abstract Introduction

Paris-Sud XI, Université de

344

4, 189212, 2007 Forecast and  

E-Print Network (OSTI)

OSD 4, 189­212, 2007 Forecast and analysis assessment through skill scores M. Tonani et al. Title Science Forecast and analysis assessment through skill scores M. Tonani 1 , N. Pinardi 2 , C. Fratianni 1 Forecast and analysis assessment through skill scores M. Tonani et al. Title Page Abstract Introduction

Paris-Sud XI, Université de

345

FINANCIAL FORECASTING USING GENETIC ALGORITHMS  

E-Print Network (OSTI)

predecessors to forecast stock prices and manage portfolios for approximately 3 years.) We examineFINANCIAL FORECASTING USING GENETIC ALGORITHMS SAM MAHFOUD and GANESH MANI LBS Capital Management entitled Genetic Algorithms for Inductive Learning). Time-series forecasting is a special type

Boetticher, Gary D.

346

Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?  

SciTech Connect

The time when energy-related carbon emissions come overwhelmingly from developed countries is coming to a close. China has already overtaken the United States as the world's leading emitter of greenhouse gas emissions. The economic growth that China has experienced is not expected to slow down significantly in the long term, which implies continued massive growth in energy demand. This paper draws on the extensive expertise from the China Energy Group at LBNL on forecasting energy consumption in China, but adds to it by exploring the dynamics of demand growth for electricity in the residential sector -- and the realistic potential for coping with it through efficiency. This paper forecasts ownership growth of each product using econometric modeling, in combination with historical trends in China. The products considered (refrigerators, air conditioners, fans, washing machines, lighting, standby power, space heaters, and water heating) account for 90percent of household electricity consumption in China. Using this method, we determine the trend and dynamics of demandgrowth and its dependence on macroeconomic drivers at a level of detail not accessible by models of a more aggregate nature. In addition, we present scenarios for reducing residential consumption through efficiency measures defined at the product level. The research takes advantage of an analytical framework developed by LBNL (BUENAS) which integrates end use technology parameters into demand forecasting and stock accounting to produce detailed efficiency scenarios, thus allowing for a technologically realistic assessment of efficiency opportunities specifically in the Chinese context.

Letschert, Virginie; McNeil, Michael A.; Zhou, Nan

2009-05-18T23:59:59.000Z

347

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

348

Forecasting world oil prices: the evolution of modeling methodologies and summary of recent projections  

SciTech Connect

This paper has three main objectives: (1) to review and summarize the varios methodologies that have been developed to explain historical oil price changes and forecast future price trends, (2) to summarize recent world oil price forecasts, and, when possible, discuss the methodologies used in formulating those forecasts, and (3) utilizing conclusions from the reviews of the modeling methodologies and the recent price forecasts, in combination with an assessment of recent and projected oil market trends, to give oil price projections for the time period 1987 to 2022. The paper argues that modeling methodologies have undergone significant evolution during the past decade as modelers increasingly recognize the complex and constantly changing structure of the world oil market. Unfortunately, a consensus about the appropriate methodology to use in formulating oil price forecasts is yet to be reached. There is, however, a general movement toward the opinion that both economic and political factors should be considered when making price projections. Likewise, there is no consensus about future oil price trends. Forecasts differ widely. However, in general, forecasts have been adjusted downwardly in recent years. Further, an overall assessment of the forecasts and recent oil market trends suggests that oil prices will remain constant in real terms for the remainder of the 1980s. Real oil prices are expected to increase by between 2 and 3% during the 1990s and beyond. Forecasters are quick to point out, however, that all forecasts are subject to significant uncertainty. 68 references, 1 figure, 6 tables.

Curlee, T.R.

1985-01-01T23:59:59.000Z

349

Residential Demand Module...................................................................................................................... 27  

E-Print Network (OSTI)

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or

unknown authors

2013-01-01T23:59:59.000Z

350

Forecast of auroral activity  

Science Conference Proceedings (OSTI)

A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting

A. T. Y. Lui

2004-01-01T23:59:59.000Z

351

History of Residential Grounding  

Science Conference Proceedings (OSTI)

This report describes the development of residential electrical service grounding practices in the United States. The report focuses on the history of the National Electrical Code (NEC), which prescribes standards for wiring practices in residences, including grounding of the building electrical service.

2002-09-19T23:59:59.000Z

352

Photovoltaics for residential applications  

DOE Green Energy (OSTI)

Information is given about the parts of a residential photovoltaic system and considerations relevant to photovoltaic power use in homes that are also tied to utility lines. In addition, factors are discussed that influence implementation, including legal and environmental factors such as solar access and building codes, insurance, utility buyback, and system longevity. (LEW)

Not Available

1984-02-01T23:59:59.000Z

353

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.

354

Residential sector: the demand for energy services  

Science Conference Proceedings (OSTI)

The purpose of this report is to project the demand for residential services, and, thereby, the demand for energy into the future. The service demands which best represent a complete breakdown of residential energy consumption is identified and estimates of the amount of energy, by fuel type, used to satisfy each service demand for an initial base year (1978) are detailed. These estimates are reported for both gross (or input) energy use and net or useful energy use, in the residential sector. The various factors which affect the consumption level for each type of energy and each identified service demand are discussed. These factors include number of households, appliance penetration, choice of fuel type, technical conversion efficiency of energy using devices, and relative energy efficiency of the building shell (extent of insulation, resistance to air infiltration, etc.). These factors are discussed relative to both the present and expected future values, for the purpose of projections. The importance of the housing stock to service demand estimation and projection and trends in housing in Illinois are discussed. How the housing stock is projected based on population and household projections is explained. The housing projections to the year 2000 are detailed. The projections of energy consumption by service demand and fuel type are contrasted with the various energy demand projections in Illinois Energy Consumption Trends: 1960 to 2000 and explains how and why the two approaches differ. (MCW)

Not Available

1981-01-01T23:59:59.000Z

355

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,

356

Context-aware parameter estimation for forecast models in the energy domain  

Science Conference Proceedings (OSTI)

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability and efficiency of energy grids. This balancing task requires accurate forecasts of future electricity consumption and production at any point in time. For ... Keywords: energy, forecasting, maintenance, parameter estimation

Lars Dannecker; Robert Schulze; Matthias Böhm; Wolfgang Lehner; Gregor Hackenbroich

2011-07-01T23:59:59.000Z

357

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

Forecasting Dangerous Inmate Misconduct: An Applications ofidentify with useful forecasting skill the very few inmatescontribute substantially to forecasting skill necessarily

Berk, Richard; Kriegler, Brian; Baek, Jong-Ho

2005-01-01T23:59:59.000Z

358

Information and Inference in Econometrics: Estimation, Testing and Forecasting  

E-Print Network (OSTI)

Application: Forecasting Equity Premium . . . . . . . . . .2.6.1 Forecasting4 Forecasting Using Supervised Factor Models 4.1

Tu, Yundong

2012-01-01T23:59:59.000Z

359

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

Forecasting Dangerous Inmate Misconduct: An Applications ofidentify with useful forecasting skill the very few inmatescontribute substantially to forecasting skill necessarily

Richard A. Berk; Brian Kriegler; Jong-Ho Baek

2011-01-01T23:59:59.000Z

360

DND: a model for forecasting electrical energy usage by water-resource subregion  

SciTech Connect

A forecast methodology was derived from principles of econometrics using exogenous variables, i.e., cost of electricity, consumer income, and price elasticity as indicators of growth for each consuming sector: residential, commercial, and industrial. The model was calibrated using forecast data submitted to the Department of Energy (DOE) by the nine Regional Electric Reliability Councils. Estimates on electrical energy usage by specific water-resource subregion were obtained by normalizing forecasted total electrical energy usage by state into per capita usage. The usage factor and data on forecasted population were applied for each water resource subregion. The results derived using the model are self-consistent and in good agreement with DOE Energy Information Administration projections. The differences that exist are largely the result of assumptions regarding specific aggregations and assignment of regional-system reliability and load factors. 8 references, 2 figures, 13 tables.

Sonnichsen, J.C. Jr.

1980-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Short-Term Energy Outlook Supplement: Summer 2013 Outlook for Residential Electric Bills  

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

Summer 2013 Outlook for Residential Summer 2013 Outlook for Residential Electric Bills June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Summer 2013 Outlook for Residential Electric Bills i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other federal agencies. June 2013 U.S. Energy Information Administration | STEO Supplement: Summer 2013 Outlook for Residential Electric Bills 1

362

Atmos Energy - Residential Natural Gas and Weatherization Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Atmos Energy - Residential Natural Gas and Weatherization Efficiency Program Atmos Energy - Residential Natural Gas and Weatherization Efficiency Program Eligibility Residential...

363

CenterPoint Energy (Gas) - Residential Efficiency Rebates (Oklahoma...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Efficiency Rebates (Oklahoma) CenterPoint Energy (Gas) - Residential Efficiency Rebates (Oklahoma) Eligibility Residential Savings For Heating & Cooling Commercial...

364

Central Georgia EMC - Residential Energy Efficiency Rebate Program...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Program Central Georgia EMC - Residential Energy Efficiency Rebate Program Eligibility Residential Savings For Home Weatherization Commercial...

365

MidAmerican Energy (Electric) - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Electric) - Residential Energy Efficiency Rebate Programs MidAmerican Energy (Electric) - Residential Energy Efficiency Rebate Programs < Back Eligibility Residential Savings...

366

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.

367

Management Earnings Forecasts and Value of Analyst Forecast Revisions  

E-Print Network (OSTI)

Prior studies evaluate the relative importance of the sources of value that financial analysts bring to the market based on the price impact of forecast revisions over the event time. We find that management earnings forecasts influence the timing and precision of analyst forecasts. More importantly, evidence suggests that prior studies ’ finding of weaker (stronger) stock-price responses to forecast revisions in the period immediately after (before) the prior-quarter earnings announcement is likely to be the artifact of a temporal pattern of management earnings forecasts over the event time. To the extent that management earnings forecasts are public disclosures, our results suggest that the relative importance of analysts ' information discovery role documented in prior studies is likely to be overstated.

Yongtae Kim; Minsup Song

2013-01-01T23:59:59.000Z

368

Commissioning Residential Ventilation Systems: A Combined Assessment of  

NLE Websites -- All DOE Office Websites (Extended Search)

Commissioning Residential Ventilation Systems: A Combined Assessment of Commissioning Residential Ventilation Systems: A Combined Assessment of Energy and Air Quality Potential Values Title Commissioning Residential Ventilation Systems: A Combined Assessment of Energy and Air Quality Potential Values Publication Type Report LBNL Report Number LBNL-5969E Year of Publication 2012 Authors Turner, William J. N., Jennifer M. Logue, and Craig P. Wray Date Published 07/2012 Keywords commissioning, energy, health, indoor air quality, residential, valuation, ventilation Abstract Due to changes in building codes, whole-house mechanical ventilation systems are being installed in new California homes. Few measurements are available, but the limited data suggest that these systems don't always perform as code and forecasts predict. Such deficiencies occur because systems are usually field assembled without design specifications, and there is no consistent process to identify and correct problems. The value of such activities in terms of reducing energy use and improving indoor air quality (IAQ) is poorly understood. Commissioning such systems when they are installed or during subsequent building retrofits is a step towards eliminating deficiencies and optimizing the tradeoff between energy use and IAQ.

369

Average Residential Price  

Gasoline and Diesel Fuel Update (EIA)

Pipeline and Distribution Use Price Citygate Price Residential Price Commercial Price Industrial Price Vehicle Fuel Price Electric Power Price Proved Reserves as of 12/31 Reserves Adjustments Reserves Revision Increases Reserves Revision Decreases Reserves Sales Reserves Acquisitions Reserves Extensions Reserves New Field Discoveries New Reservoir Discoveries in Old Fields Estimated Production Number of Producing Gas Wells Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production Natural Gas Processed NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals LNG Storage Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

370

Residential Heating Oil Prices  

Gasoline and Diesel Fuel Update (EIA)

This chart highlights residential heating oil prices for the current and This chart highlights residential heating oil prices for the current and past heating season. As you can see, prices have started the heating season, about 40 to 50 cents per gallon higher than last year at this time. The data presented are from EIA's State Heating Oil and Propane Program. We normally collect and publish this data twice a month, but given the low stocks and high prices, we started tracking the prices weekly. These data will also be used to determine the price trigger mechanism for the Northeast Heating Oil Reserve. The data are published at a State and regional level on our web site. The slide is to give you some perspective of what is happening in these markets, since you probably will get a number of calls from local residents about their heating fuels bills

371

Average Residential Price  

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

Pipeline and Distribution Use Price Citygate Price Residential Price Commercial Price Industrial Price Vehicle Fuel Price Electric Power Price Proved Reserves as of 12/31 Reserves Adjustments Reserves Revision Increases Reserves Revision Decreases Reserves Sales Reserves Acquisitions Reserves Extensions Reserves New Field Discoveries New Reservoir Discoveries in Old Fields Estimated Production Number of Producing Gas Wells Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production Natural Gas Processed NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals LNG Storage Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

372

Residential Energy Audits  

E-Print Network (OSTI)

A series of events coupled with the last five years experience performing Residential Conservation Service (RCS) audits have resulted in renewed efforts by utilities to evaluate the role of residential energy audits. There are utilities where the RCS program is considered very successful; however, the majority of utilities have found that the costs far exceed the benefits. Typically, the response rates are low (less than 1% per year for Texas utilities), the audits primarily reach upper income persons, and consumers only implement the low-cost recommendations. The Texas PUC is on record as being opposed to the RCS as well as the Commercial and Apartment Conservation Service (CACS) and now requires Energy Efficiency Plans with detailed cost and savings information on utility end user programs.

Brown, W.

1985-01-01T23:59:59.000Z

373

Residential Programmable Communicating Thermostats  

Science Conference Proceedings (OSTI)

Residential programmable communicating thermostats (PCTs) enable demand response and offer a convenient energy management option for the consumer. PCTs allow customers to program and control temperature set-points remotely, primarily through the Internet. Additionally, some of these thermostats can be remotely controlled by utilities or third parties to curtail heating and cooling loads during periods of peak electricity demand. This Technology Brief, prepared for the Energy Efficiency Initiative, presen...

2007-12-05T23:59:59.000Z

374

Detailed residential electric determination  

DOE Green Energy (OSTI)

Data on residential loads has been collected from four residences in real time. The data, measured at 5-second intervals for 53 days of continuous operation, were statistically characterized. An algorithm was developed and incorporated into the modeling code SOLCEL. Performance simulations with SOLCEL using these data as well as previous data collected over longer time intervals indicate that no significant errors in system value are introduced through the use of long-term average data.

Not Available

1984-06-01T23:59:59.000Z

375

Residential Energy Display Devices  

Science Conference Proceedings (OSTI)

Residential energy display devices provide direct feedback to consumers about their electricity use and cost, direct feedback that potentially can help customers manage electricity consumption. EPRI tested five different stand-alone display devices in its Energy Efficiency and Demand Response Living Laboratory to assess whether devices functioned according to manufacturer specifications. In addition to providing results of these tests, this Technology Brief describes how display devices operate, summariz...

2008-06-20T23:59:59.000Z

376

OG&E - Residential Energy Efficiency Program | Department of...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

OG&E - Residential Energy Efficiency Program OG&E - Residential Energy Efficiency Program Eligibility Low-Income Residential Residential Savings For Heating & Cooling Commercial...

377

Chapter 11 Forecasting breaks and forecasting during breaks  

E-Print Network (OSTI)

Success in accurately forecasting breaks requires that they are predictable from relevant information available at the forecast origin using an appropriate model form, which can be selected and estimated before the break. To clarify the roles of these six necessary conditions, we distinguish between the information set for ‘normal forces ’ and the one for ‘break drivers’, then outline sources of potential information. Relevant non-linear, dynamic models facing multiple breaks can have more candidate variables than observations, so we discuss automatic model selection. As a failure to accurately forecast breaks remains likely, we augment our strategy by modelling breaks during their progress, and consider robust forecasting devices.

Jennifer L. Castle; Nicholas W. P. Fawcett; David F. Hendry

2011-01-01T23:59:59.000Z

378

Free World energy survey: historical overview and long-term forecast  

SciTech Connect

This report gives a historical overview of international energy markets from the 1950s to date, and an analysis of future energy prices, economic growth, and potential supply instabilities. Forecasts of energy demand by region and fuel type are provided.

1983-01-01T23:59:59.000Z

379

On the Use of Mesoscale and Cloud-Scale Models in Operational Forecasting  

Science Conference Proceedings (OSTI)

In the near future, the technological capability will be available to use mesoscale and cloud-scale numerical models for forecasting convective weather in operational meteorology. We address some of the issues concerning effective utilization of ...

Harold E. Brooks; Charles A. Doswell III; Robert A. Maddox

1992-03-01T23:59:59.000Z

380

NeuroInflow: The New Model to Forecast Average Monthly Inflow  

Science Conference Proceedings (OSTI)

In utilities using a mixture of hydroelectric and non-hydroelectricpower, the economics of the hydroelectricplants depend upon the reservoir height and the inflowinto the reservoir for several months into the future.Accurate forecasts of reservoir inflow ...

Mêuser Valença; Teresa Ludermir

2002-11-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Representing Serial Correlation of Meteorological Events and Forecasts in Dynamic Decision–Analytic Models  

Science Conference Proceedings (OSTI)

A recursive solution for optimal sequences of decisions given uncertainty in future weather events, and forecasts of those events, is presented. The formulation incorporates a representation of the autocorrelation that is typically exhibited. The ...

Daniel S. Wilks

1991-07-01T23:59:59.000Z

382

Forecast Technical Document Growing Stock Volume  

E-Print Network (OSTI)

Forecast Technical Document Growing Stock Volume Forecasts A document describing how growing stock (`standing') volume is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Growing stock volume forecasts Background A forecast of standing volume (or

383

Forecasting Uncertain Hotel Room Demand  

E-Print Network (OSTI)

Economic systems are characterized by increasing uncertainty in their dynamics. This increasing uncertainty is likely to incur bad decisions that can be costly in financial terms. This makes forecasting of uncertain economic variables an instrumental activity in any organization. This paper takes the hotel industry as a practical application of forecasting using the Holt-Winters method. The problem here is to forecast the uncertain demand for rooms at a hotel for each arrival day. Forecasting is part of hotel revenue management system whose objective is to maximize the revenue by making decisions regarding when to make rooms available for customers and at what price. The forecast approach discussed in this paper is based on quantitative models and does not incorporate management expertise. Even though, forecast results are found to be satisfactory for certain days, this is not the case for other arrival days. It is believed that human judgment is important when dealing with ...

Mihir Rajopadhye Mounir; Mounir Ben Ghaliay; Paul P. Wang; Timothy Baker; Craig V. Eister

2001-01-01T23:59:59.000Z

384

Building Technologies Office: About Residential Building Programs  

NLE Websites -- All DOE Office Websites (Extended Search)

About Residential About Residential Building Programs to someone by E-mail Share Building Technologies Office: About Residential Building Programs on Facebook Tweet about Building Technologies Office: About Residential Building Programs on Twitter Bookmark Building Technologies Office: About Residential Building Programs on Google Bookmark Building Technologies Office: About Residential Building Programs on Delicious Rank Building Technologies Office: About Residential Building Programs on Digg Find More places to share Building Technologies Office: About Residential Building Programs on AddThis.com... About Take Action to Save Energy Partner With DOE Activities Technology Research, Standards, & Codes Popular Residential Links Success Stories Previous Next Warming Up to Pump Heat.

385

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 technologies. This paper briefly discusses the observed patterns of the diffusion of new' technologies and the determinants (both sociological and economic) which have been proposed to explain the variation in the diffusion rates. Existing market penetration models are reviewed and their capability to forecast the use of conservation technologies is assessed using a set of criteria developed for this purpose. The reasoning behind the choice of criteria is discussed. The criteria includes the range of hypothesized influences to market penetration that are incorporated into the models and the applicability of the available parameter estimates. The attributes of our methodology and forecasting model choice (a behavioral lag equation developed by Mathtech, Inc.), are displayed using a list of the judgment criteria. This method was used to forecast the use of electricity conservation technologies in industries located in the Pacific Northwest for the Bonneville Power Administration.

Lang, K.

1982-01-01T23:59:59.000Z

386

Acting Globally: Potential Carbon Emissions Mitigation Impacts from an International Standards and Labelling Program  

E-Print Network (OSTI)

2008). The Boom of Electricity Demand in the residential2005). Forecasting Electricity Demand in Developingwith Residential Electricity Demand in India's Future - How

Letschert, Virginie E.

2010-01-01T23:59:59.000Z

387

Building simulation weather forecast files for predictive control strategies  

Science Conference Proceedings (OSTI)

Model-Based Predictive Control (MPC) has received significant attention in recent years as a tool for load management in buildings. MPC is based on predicting the response of a system based on knowledge of future inputs, such as weather and occupancy. ... Keywords: EPW files, building simulation, predictive control, weather forecast

José. A. Candanedo; Éric Paradis; Meli Stylianou

2013-04-01T23:59:59.000Z

388

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network (OSTI)

of future contributions from various emerging transportation fuels and technologies is unknown. PotentiallyCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY AND TRANSPORTATION DIVISION B. B. Blevins Executive Director DISCLAIMER This report was prepared by a California

389

Application of Polynomial Neural Networks to Exchange Rate Forecasting  

Science Conference Proceedings (OSTI)

This research investigates the use of Ridge Polynomial Neural Network (RPNN) as non-linear prediction model to forecast the future trends of financial time series. The network was used for the prediction of one step ahead and five steps ahead of two ... Keywords: Dynamic Ridge Polynomial Neural Network, Financial time series, Multilayer Perceptron, Ridge Polynomial Neural Network

R. Ghazali; A. J. Hussain; M. N. Mohd. Salleh

2008-11-01T23:59:59.000Z

390

Application of DSM evaluation studies to utility forecasting and planning  

SciTech Connect

Utilities and their customers have made substantial investments in utility demand-side management (DSM) programs. These DSM programs also represent a substantial electricity resource. DSM program performance has been studied more systematically in recent years than over any previous period. DSM program evaluations are traditionally targeted to meet the program manager`s need for information on program costs and performance and, more recently, to verify savings to regulators for incentive awards and lost revenue recovery. Yet evaluations may also be used to produce results relevant to utility forecasting and planning. Applying evaluation results is especially important for utilities with substantial current and future commitments to acquiring demand-side resources. This report discusses the application of evaluation results to utility forecasting and planning. The report has three objectives. First, we identify what demand forecasters, DSM forecasters, and resource planners want to learn from evaluations. Second, we identify and describe the major obstacles and problems associated with applying evaluation results and illustrate many of these issues through a specific evaluation application exercise. Finally, we suggest approaches for addressing these major problems. The report summarizes results from interviews with utilities, regulators, and consultants to determine how the industry currently applies evaluation results in forecasting and planning. The report also includes results from case studies of Sacramento Municipal Utility District and Southern California Edison Company, utilities with large DSM programs and active evaluation efforts. Finally, we draw on a specific application exercise in which we used a set of impact evaluations to revise a utility DSM forecast.

Baxter, L.W.

1995-02-01T23:59:59.000Z

391

Health effects of residential wood combustion: survey of knowledge and research  

DOE Green Energy (OSTI)

Health and safety issues related to residential wood burning are examined. Current research and findings are also described, and research status is assessed in terms of future health and safety requirements. (MHR)

None

1980-09-01T23:59:59.000Z

392

Aviation forecasting and systems analyses  

SciTech Connect

The 9 papers in this report deal with the following areas: method of allocating airport runway slots; method for forecasting general aviation activity; air traffic control network-planning model based on second-order Markov chains; analyzing ticket-choice decisions of air travelers; assessing the safety and risk of air traffic control systems: risk estimation from rare events; forecasts of aviation fuel consumption in Virginia; estimating the market share of international air carriers; forecasts of passenger and air-cargo activity at Logan International Airport; and forecasting method for general aviation aircraft and their activity.

Geisinger, K.E.; Brander, J.R.G.; Wilson, F.R.; Kohn, H.M.; Polhemus, N.W.

1980-01-01T23:59:59.000Z

393

Studies of inflation and forecasting.  

E-Print Network (OSTI)

??This dissertation contains five research papers in the area of applied econometrics. The two broad themes of the research are inflation and forecasting. The first… (more)

Bermingham, Colin

2011-01-01T23:59:59.000Z

394

UWIG Forecasting Workshop -- Albany (Presentation)  

SciTech Connect

This presentation describes the importance of good forecasting for variable generation, the different approaches used by industry, and the importance of validated high-quality data.

Lew, D.

2011-04-01T23:59:59.000Z

395

RESIDENTIAL ENERGY CONSUMPTION SURVEY 1997  

U.S. Energy Information Administration (EIA)

RESIDENTIAL ENERGY CONSUMPTION SURVEY 1997. OVERVIEW: MOST POPULOUS STATES ... Homes with air-conditioning: 95%... with a central air-conditioning system: 83%

396

Residential ventilation standards scoping study  

E-Print Network (OSTI)

of new residences. The Hawaii Model Energy Code (HMEC) is aHawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Residential Energy Code

McKone, Thomas E.; Sherman, Max H.

2003-01-01T23:59:59.000Z

397

Natural Gas Residential Choice Programs  

U.S. Energy Information Administration (EIA)

Status of Natural Gas Residential Choice Programs by State as of December 2008 (Click on a State or its abbreviation for more information about that ...

398

2001 Residential Energy Consumption Survey  

U.S. Energy Information Administration (EIA)

Residential Energy Consumption Survey ... Office of Management and Budget, Washington, DC 20503. Form EIA-457A (2001) Form Approval: OMB No. 1905-0092 ...

399

Residential Price - Local Distribution Companies  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per ...

400

Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets  

Science Conference Proceedings (OSTI)

This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.

Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

2005-02-09T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

On the Prediction of Forecast Skill  

Science Conference Proceedings (OSTI)

Using 10-day forecast 500 mb height data from the last 7 yr, the potential to predict the skill of numerical weather forecasts is discussed. Four possible predictor sets are described. The first, giving the consistency between adjacent forecasts, ...

T. N. Palmer; S. Tibaldi

1988-12-01T23:59:59.000Z

402

Equitable Skill Scores for Categorical Forecasts  

Science Conference Proceedings (OSTI)

Many skill scores used to evaluate categorical forecasts of discrete variables are inequitable, in the sense that constant forecasts of some events lead to better scores than constant forecasts of other events. Inequitable skill scores may ...

Lev S. Gandin; Allan H. Murphy

1992-02-01T23:59:59.000Z

403

Evaluation of errors in national energy forecasts.  

E-Print Network (OSTI)

??Energy forecasts are widely used by the U.S. government, politicians, think tanks, and utility companies. While short-term forecasts were reasonably accurate, medium and long-range forecasts… (more)

Sakva, Denys

2005-01-01T23:59:59.000Z

404

What Is the True Value of Forecasts?  

Science Conference Proceedings (OSTI)

Understanding the economic value of weather and climate forecasts is of tremendous practical importance. Traditional models that have attempted to gauge forecast value have focused on a best-case scenario, in which forecast users are assumed to ...

Antony Millner

2009-10-01T23:59:59.000Z

405

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts ...

Mingyue Chen; Wanqiu Wang; Arun Kumar

2013-10-01T23:59:59.000Z

406

Whither the Weather Analysis and Forecasting Process?  

Science Conference Proceedings (OSTI)

An argument is made that if human forecasters are to continue to maintain a skill advantage over steadily improving model and guidance forecasts, then ways have to be found to prevent the deterioration of forecaster skills through disuse. The ...

Lance F. Bosart

2003-06-01T23:59:59.000Z

407

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is presented. The focus of the analysis is on the construction of lagged ensemble forecasts ...

Mingyue Chen; Wanqiu Wang; Arun Kumar

408

Improving Forecast Communication: Linguistic and Cultural Considerations  

Science Conference Proceedings (OSTI)

One goal of weather and climate forecasting is to inform decision making. Effective communication of forecasts to various sectors of the public is essential for meeting that goal, yet studies repeatedly show that forecasts are not well understood ...

Karen Pennesi

2007-07-01T23:59:59.000Z

409

Ensemble Cloud Model Applications to Forecasting Thunderstorms  

Science Conference Proceedings (OSTI)

A cloud model ensemble forecasting approach is developed to create forecasts that describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for anticipating severe ...

Kimberly L. Elmore; David J. Stensrud; Kenneth C. Crawford

2002-04-01T23:59:59.000Z

410

Probabilistic Verification of Monthly Temperature Forecasts  

Science Conference Proceedings (OSTI)

Monthly forecasting bridges the gap between medium-range weather forecasting and seasonal predictions. While such forecasts in the prediction range of 1–4 weeks are vital to many applications in the context of weather and climate risk management, ...

Andreas P. Weigel; Daniel Baggenstos; Mark A. Liniger; Frédéric Vitart; Christof Appenzeller

2008-12-01T23:59:59.000Z

411

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

412

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

413

A Forecast for the California Labor Market  

E-Print Network (OSTI)

issue for the state. A Forecast for the California Laborto Go? ” The UCLA Anderson Forecast for the Nation andAngeles: UCLA Anderson Forecast: Nation 1.1 – 1.9. Dhawan,

Mitchell, Daniel J. B.

2001-01-01T23:59:59.000Z

414

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400.................................................................................. 9 Sources of Forecast Error....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2

415

Calibration of Probabilistic Forecasts of Binary Events  

Science Conference Proceedings (OSTI)

Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality of such forecasts can ...

Cristina Primo; Christopher A. T. Ferro; Ian T. Jolliffe; David B. Stephenson

2009-03-01T23:59:59.000Z

416

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network (OSTI)

1 CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST QUALITY: EVIDENCE FROM FRENCH IPOS Anis attributes, ownership retained, auditor quality, and underwriter reputation and management earnings forecast quality measured by management earnings forecast accuracy and bias. Using 117 French IPOs, we find

Paris-Sud XI, Université de

417

Forecasting women's apparel sales using mathematical  

E-Print Network (OSTI)

Forecasting women's apparel sales using mathematical modeling Celia Frank and Ashish Garg, USA Les Sztandera Philadelphia University, Philadelphia, PA, USA Keywords Apparel, Forecasting average (MA), auto- regression (AR), or combinations of them are used for forecasting sales. Since

Raheja, Amar

418

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Conference Proceedings (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

419

Forecasting Electric Vehicle Costs with Experience Curves  

E-Print Network (OSTI)

April, 5. R 2~1. Dino. "Forecasting the Price Evolution of 1ElectromcProducts," Ioumal of Forecasting, ¥oL4, No I, 1985.costs and a set of forecasting tools that can be refined as

Lipman, Timonthy E.; Sperling, Daniel

2001-01-01T23:59:59.000Z

420

Calibration of Probabilistic Quantitative Precipitation Forecasts  

Science Conference Proceedings (OSTI)

From 1 August 1990 to 31 July 1995, the Weather Service Forecast Office in Pittsburgh prepared 6159 probabilistic quantitative precipitation forecasts. Forecasts were made twice a day for 24-h periods beginning at 0000 and 1200 UTC for two river ...

Roman Krzysztofowicz; Ashley A. Sigrest

1999-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts future residential" 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

Evaluating Probabilistic Forecasts Using Information Theory  

Science Conference Proceedings (OSTI)

The problem of assessing the quality of an operational forecasting system that produces probabilistic forecasts is addressed using information theory. A measure of the quality of the forecasting scheme, based on the amount of a data compression ...

Mark S. Roulston; Leonard A. Smith

2002-06-01T23:59:59.000Z

422

Virtual Floe Ice Drift Forecast Model Intercomparison  

Science Conference Proceedings (OSTI)

Both sea ice forecast models and methods to measure their skill are needed for operational sea ice forecasting. Two simple sea ice models are described and tested here. Four different measures of skill are also tested. The forecasts from the ...

Robert W. Grumbine

1998-09-01T23:59:59.000Z

423

Spatial and Temporal Dynamics: Residential Development Process  

E-Print Network (OSTI)

A lack of empirical evidence to understand neighborhood and residential development processes within neighborhoods has challenged urban planners’ ability to influence the course of future land development. The main objectives of this study were to examine neighborhood and residential development patterns and investigate dynamic processes in northwest Harris County, Texas, along the U.S. Highway 290 transportation corridor from 1945 to 2006. Researchers have identified different patterns of land development: leapfrog, contagion and infill development. However, because of the fuzziness in neighborhood and residential development patterns, the nominal classifications of development patterns are limited in their potential to characterize development patterns both on neighborhood and parcel levels; their applications for development processes and its impacts are even more limited. This study presents a quantitative approach for measuring development patterns by characterizing neighborhood development patterns as a function of spatial distance and temporal lapse time from the closest existing neighborhood to new neighborhood(s). The analysis in this study was based on disaggregated parcel data provided by the Harris County Appraisal District (HCAD) real estate and property records. The quantitative measures of neighborhood development patterns and processes within each pattern of neighborhood were derived by aggregating parcel level data into neighborhood level. This study developed the Long-term Trend of Development Model (LTDM) to classify neighborhood and residential development patterns based on spatial distance and temporal lapse time from existing neighborhoods to new neighborhood(s) each year to examine development processes. Regression analysis was used to identify the relationship between neighborhood patterns and residential development processes. This study found that development patterns can be measured quantitatively with spatial and temporal relationships between prior and new development at the neighborhood level. Empirical evidence supported the hypothesis that leapfrog neighborhood development triggers neighborhood development, contagion follows leapfrog neighborhood quickly, and infill follows contagion after a lapsed time. Residential development patterns in each pattern of neighborhood showed discrete development processes. Age of neighborhood can be used to predict development pressures and growth. In this process, physical and social infrastructure is involved, therefore, development process is best observed on the neighborhood level.

Park, Joung Im

2010-12-01T23:59:59.000Z

424

Evaluating Density Forecasts: Forecast Combinations, Model Mixtures, Calibration and Sharpness  

E-Print Network (OSTI)

In a recent article Gneiting, Balabdaoui and Raftery (JRSSB, 2007) propose the criterion of sharpness for the evaluation of predictive distributions or density forecasts. They motivate their proposal by an example in which standard evaluation procedures based on probability integral transforms cannot distinguish between the ideal forecast and several competing forecasts. In this paper we show that their example has some unrealistic features from the perspective of the time-series forecasting literature, hence it is an insecure foundation for their argument that existing calibration procedures are inadequate in practice. We present an alternative, more realistic example in which relevant statistical methods, including information-based methods, provide the required discrimination between competing forecasts. We conclude that there is no need for a subsidiary criterion of sharpness.

James Mitchell; Kenneth F. Wallis

2008-01-01T23:59:59.000Z

425

The evolution of consensus in macroeconomic forecasting  

E-Print Network (OSTI)

When professional forecasters repeatedly forecast macroeconomic variables, their forecasts may converge over time towards a consensus. The evolution of consensus is analyzed with Blue Chip data under a parametric polynomial decay function that permits flexibility in the decay path. For the most part, this specification fits the data. We test whether forecast differences decay to zero at the same point in time for a panel of forecasters, and discuss possible explanations for this, along with its implications for studies using panels of forecasters.

Allan W. Gregory; James Yetman; Jel Codes C E; Robert Eggert; Fred Joutz

2004-01-01T23:59:59.000Z

426

Property For Homeowners- Residential  

E-Print Network (OSTI)

Targets improvements on certain types of property that will save energy when compared to the property which they replaced. • Provides for a uniform credit of 30 percent of the cost of qualifying improvements. • Cap for all tax years is now $1,500, three times the prior legislation provided. • Temporarily can rely on existing manufacturer certifications or appropriate Energy Star labels for purchasing qualified products. For Homeowners- Expanded Energy Efficient Property Tax Credit for Residences • Residential energy efficient property credit has expanded to include more alternate energy equipment.

Tom Sheaffer; Stakeholder Liaison; New Clean Renewable Energy Bonds

2009-01-01T23:59:59.000Z

427

ASHRAE and residential ventilation  

SciTech Connect

In the last quarter of a century, the western world has become increasingly aware of environmental threats to health and safety. During this period, people psychologically retreated away from outdoors hazards such as pesticides, smog, lead, oil spills, and dioxin to the seeming security of their homes. However, the indoor environment may not be healthier than the outdoor environment, as has become more apparent over the past few years with issues such as mold, formaldehyde, and sick-building syndrome. While the built human environment has changed substantially over the past 10,000 years, human biology has not; poor indoor air quality creates health risks and can be uncomfortable. The human race has found, over time, that it is essential to manage the indoor environments of their homes. ASHRAE has long been in the business of ventilation, but most of the focus of that effort has been in the area of commercial and institutional buildings. Residential ventilation was traditionally not a major concern because it was felt that, between operable windows and envelope leakage, people were getting enough outside air in their homes. In the quarter of a century since the first oil shock, houses have gotten much more energy efficient. At the same time, the kinds of materials and functions in houses changed in character in response to people's needs. People became more environmentally conscious and aware not only about the resources they were consuming but about the environment in which they lived. All of these factors contributed to an increasing level of public concern about residential indoor air quality and ventilation. Where once there was an easy feeling about the residential indoor environment, there is now a desire to define levels of acceptability and performance. Many institutions--both public and private--have interests in Indoor Air Quality (IAQ), but ASHRAE, as the professional society that has had ventilation as part of its mission for over 100 years, is the logical place to provide leadership. This leadership has been demonstrated most recently by the publication of the first nationally recognized standard on ventilation in homes, ASHRAE Standard 62.2-2003, which builds on work that has been part of ASHRAE for many years and will presumably continue. Homeowners and occupants, which includes virtually all of us, will benefit from the application of Standard 62.2 and use of the top ten list. This activity is exactly the kind of benefit to society that the founders of ASHRAE envisioned and is consistent with ASHRAE's mission and vision. ASHRAE members should be proud of their Society for taking leadership in residential ventilation.

Sherman, Max H.

2003-10-01T23:59:59.000Z

428

Evaluation of evolving residential electricity tariffs  

E-Print Network (OSTI)

evolving residential electricity tariffs Judy Lai, Nicholasevolving residential electricity tariffs Judy Lai – Seniortariffs and explanation of baseline Until the middle of 2001, PG&E employed a two-tiered pricing structure for residential electricity

Lai, Judy

2011-01-01T23:59:59.000Z

429

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

430

Improving Forecasting: A plea for historical retrospectives  

NLE Websites -- All DOE Office Websites (Extended Search)

Improving Forecasting: A plea for historical retrospectives Title Improving Forecasting: A plea for historical retrospectives Publication Type Journal Article Year of Publication...

431

Background pollution forecast over bulgaria  

Science Conference Proceedings (OSTI)

Both, the current level of air pollution studies and social needs in the country, are in a stage mature enough for creating Bulgarian Chemical Weather Forecasting and Information System The system is foreseen to provide in real time forecast of the spatial/temporal ...

D. Syrakov; K. Ganev; M. Prodanova; N. Miloshev; G. Jordanov; E. Katragkou; D. Melas; A. Poupkou; K. Markakis

2009-06-01T23:59:59.000Z

432

Frequency Dependence in Forecast Skill  

Science Conference Proceedings (OSTI)

A method is proposed to calculate measures of forecast skill for high, medium and low temporal frequency variations in the atmosphere. This method is applied to a series of 128 consecutive 1 to 10-day forecasts produced at NMC with their ...

H. M. van Den Dool; Suranjana Saha

1990-01-01T23:59:59.000Z

433

Modeling diffusion of electrical appliances in the residential sector  

SciTech Connect

This paper presents a methodology for modeling residential appliance uptake as a function of root macroeconomic drivers. The analysis concentrates on four major energy end uses in the residential sector: refrigerators, washing machines, televisions and air conditioners. The model employs linear regression analysis to parameterize appliance ownership in terms of household income, urbanization and electrification rates according to a standard binary choice (logistic) function. The underlying household appliance ownership data are gathered from a variety of sources including energy consumption and more general standard of living surveys. These data span a wide range of countries, including many developing countries for which appliance ownership is currently low, but likely to grow significantly over the next decades as a result of economic development. The result is a 'global' parameterization of appliance ownership rates as a function of widely available macroeconomic variables for the four appliances studied, which provides a reliable basis for interpolation where data are not available, and forecasting of ownership rates on a global scale. The main value of this method is to form the foundation of bottom-up energy demand forecasts, project energy-related greenhouse gas emissions, and allow for the construction of detailed emissions mitigation scenarios.

McNeil, Michael A.; Letschert, Virginie E.

2009-11-22T23:59:59.000Z

434

Residential Energy Consumption Survey:  

Gasoline and Diesel Fuel Update (EIA)

E/EIA-0262/2 E/EIA-0262/2 Residential Energy Consumption Survey: 1978-1980 Consumption and Expenditures Part II: Regional Data May 1981 U.S. Department of Energy Energy Information Administration Assistant Administrator for Program Development Office of the Consumption Data System Residential and Commercial Data Systems Division -T8-aa * N uojssaooy 'SOS^-m (£03) ao£ 5925 'uofSfAfQ s^onpojj aa^ndmoo - aojAaag T BU T3gN am rcoj? aig^IT^^ '(adBx Q-naugBH) TOO/T8-JQ/30Q 30^703 OQ ' d jo :moaj ajqBfT^A^ 3J^ sjaodaa aAoqe aqa jo 's-TZTOO-eoo-Tgo 'ON ^ois odo 'g^zo-via/aoQ 'TBST Sujpjjng rXaAang uojidmnsuoo XSaaug sSu-ppjprig ON ^oo^s OdO '^/ZOZO-Via/aOQ *086T aunr '6L6I ?sn§ny og aunf ' jo suja^Bd uoj^dmnsuoo :XaAjng uo^^dmnsuoQ XSaaug OS '9$ '6-ieTOO- 00-T90 OdD 'S/ZOZO-Via/aOa C

435

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

436

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

437

Detroit Public Lighting Department - Residential Energy Wise...  

Open Energy Info (EERE)

Multi-Family Residential, Residential Eligible Technologies Ceiling Fan, Lighting, LED Lighting Active Incentive Yes Implementing Sector Utility Energy Category Energy...

438

Building Technologies Office: Residential Building Activities  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential Building Residential Building Activities to someone by E-mail Share Building Technologies Office: Residential Building Activities on Facebook Tweet about Building Technologies Office: Residential Building Activities on Twitter Bookmark Building Technologies Office: Residential Building Activities on Google Bookmark Building Technologies Office: Residential Building Activities on Delicious Rank Building Technologies Office: Residential Building Activities on Digg Find More places to share Building Technologies Office: Residential Building Activities on AddThis.com... About Take Action to Save Energy Partner With DOE Activities Solar Decathlon Building America Home Energy Score Home Performance with ENERGY STAR Better Buildings Neighborhood Program Challenge Home Guidelines for Home Energy Professionals

439

Better Buildings Neighborhood Program: Better Buildings Residential...  

NLE Websites -- All DOE Office Websites (Extended Search)

Better Buildings Residential Network to someone by E-mail Share Better Buildings Neighborhood Program: Better Buildings Residential Network on Facebook Tweet about Better Buildings...

440

Energy Efficiency Report: Chapter 3: Residential Sector  

U.S. Energy Information Administration (EIA)

3. The Residential Sector Introduction. More than 90 million single-family, multifamily, and mobile home households encompass the residential sector.

Note: This page contains sample records for the topic "forecasts future residential" 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

CONTAM Libraries - Appendix C2: Miscellaneous Residential ...  

Science Conference Proceedings (OSTI)

... item, C2. CPEN_RAV, Residential, HVAC ceiling penetration, typical value, ELA4, 5 cm 2 /item, C2. CPEN_RMN, Residential, ...

442

Peak Electricity Impacts of Residential Water Use  

NLE Websites -- All DOE Office Websites (Extended Search)

Peak Electricity Impacts of Residential Water Use Title Peak Electricity Impacts of Residential Water Use Publication Type Report LBNL Report Number LBNL-5736E Year of Publication...

443

Cedarburg Light & Water Utility - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Cedarburg Light & Water Utility - Residential Energy Efficiency Rebate Program Cedarburg Light & Water Utility - Residential Energy Efficiency Rebate Program Eligibility Low-Income...

444

Performance Criteria for Residential Zero Energy Windows  

NLE Websites -- All DOE Office Websites (Extended Search)

Performance Criteria for Residential Zero Energy Windows Title Performance Criteria for Residential Zero Energy Windows Publication Type Conference Paper LBNL Report Number...

445

RESIDENTIAL WEATHERIZATION SPECIFICATIONS August 30, 2011  

E-Print Network (OSTI)

RESIDENTIAL WEATHERIZATION SPECIFICATIONS August 30, 2011 Index to Sections Section Page I. GENERAL............................................................................................35 #12;1 I. GENERAL SPECIFICATIONS 1. These specifications apply to existing residential (retro

446

Residential Code Development | Building Energy Codes Program  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential Code Development Subscribe to updates To receive news and updates about code development activities subscribe to the BECP Mailing List. The model residential building...

447

Residential Commissioning: A Review of Related Literature  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential Commissioning: A Review of Related Literature Title Residential Commissioning: A Review of Related Literature Publication Type Report LBNL Report Number LBNL-44535 Year...

448

NREL: Energy Systems Integration - Residential and Commercial...  

NLE Websites -- All DOE Office Websites (Extended Search)

Residential and Commercial Integration Energy systems integration R&D at the small-scale, residential and commercial integration level encompasses diverse technologies such as...

449

Residential Sector Demand Module 1995, Model Documentation  

Reports and Publications (EIA)

This updated version of the NEMS Residential Module Documentation includes changesmade to the residential module for the production of the Annual Energy Outlook 1995.

John H. Cymbalsky

1995-03-01T23:59:59.000Z

450

Minnesota Energy Resources (Gas) - Residential Energy Efficiency...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Agencies You are here Home Savings Minnesota Energy Resources (Gas) - Residential Energy Efficiency Rebate Program Minnesota Energy Resources (Gas) - Residential Energy...

451

Avista Utilities (Electric) - Residential Energy Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

residential customers to save energy in eligible homes. Offers apply to residential homeowners in Idaho who heat homes primarily with Avista electricity Incentives vary depending...

452

National Grid (Electric) - Residential Energy Efficiency Rebate...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Residential Energy Efficiency Rebate Programs (Upstate New York) National Grid (Electric) - Residential Energy Efficiency Rebate Programs (Upstate New York) Eligibility Installer...

453

Does Mixing Make Residential Ventilation More Effective?  

E-Print Network (OSTI)

Does Mixing Make Residential Ventilation More Effective? Maxmanufacturer, or otherwise, does not necessarily constitutethe University of California. Does Mixing Make Residential

Sherman, Max

2011-01-01T23:59:59.000Z

454

Residential energy gateway system in smart grid.  

E-Print Network (OSTI)

??This project discusses about the residential energy gateway in the Smart Grid. A residential energy gateway is a critical component in the Home Energy Management… (more)

Thirumurthy, Vinod Govindswamy

2010-01-01T23:59:59.000Z

455

Residential Energy Demand Reduction Analysis and Monitoring Platform - REDRAMP  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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

456

Adapting state and national electricity consumption forecasting methods to utility service areas. Final report  

SciTech Connect

This report summarizes the experiences of six utilities (Florida Power and Light Co., Municipal Electric Authority of Georgia, Philadelphia Electric Co., Public Service Co. of Colorado, Sacramento Municipal Utility District, and TVA) in adapting to their service territories models that were developed for forecasting loads on a national or regional basis. The models examined were of both end-use and econometric design and included the three major customer classes: residential, commercial, and industrial.

Swift, M.A.

1984-07-01T23:59:59.000Z

457

Sixth Northwest Conservation and Electric Power Plan Appendix D: Wholesale Electricity Price Forecast  

E-Print Network (OSTI)

Forecast Introduction................................................................... 16 The Base Case Forecast..................................................................... 16 Base Case Price Forecast

458

Model documentation: electricity market module. [15 year forecasts  

SciTech Connect

This report documents the electricity market model. This model is a component of the Intermediate Future Forecasting System (IFFS), the energy market model used to provide projections of energy markets up to 15 years into the future. The electricity market model was developed by the Supply Analysis and Integration Branch as part of building the larger system. This report is written for an audience consisting of mathematical economists, statisticians, operations research analysts, and utility planners. This report contains an overview and a mathematical specification of the electricity market module. It includes a description of the model logic and the individual subroutines in the computer code. A companion document Intermediate Future Forecasting System: Executive Summary (DOE/EIA-430) provides an overview of the components in IFFS and their linkages. 22 figures, 2 tables.

Sanders, R.C.; Murphy, F.H.

1984-12-01T23:59:59.000Z

459

Load transfer coupling regression curve fitting for distribution load forecasting  

SciTech Connect

The planning of distribution facilities requires forecasts of future substation and feeder loads. Extrapolation based on a curve fit to past annual peak loads is currently the most popular manner of accomplishing this forecast. Curve fitting suffers badly from data shifts caused by switching as loads are routinely moved from one substation to another during the course of utility operations. This switching contaminates the data, reducing forecast accuracy. A new regression application reduces error due to these transfers by over an order of magnitude. A key to the usefulness of this method is that the amount of the transfer, and its direction (whether it was to or from a substation), is not a required input. The new technique, aspects of computer implementation of it, and a series of tests showing its advantage over normal multiple regression methods are given.

Willis, H.L.; Powell, R.W.

1984-05-01T23:59:59.000Z

460

Residential Ventilation & Energy  

NLE Websites -- All DOE Office Websites (Extended Search)

5 5 Residential Ventilation & Energy Figure 1: Annual Average Ventilation Costs of the Current U.S. Single-Family Housing Stock ($/year/house). Infiltration and ventilation in dwellings is conventionally believed to account for one-third to one-half of space conditioning energy. Unfortunately, there is not a great deal of measurement data or analysis to substantiate this assumption. As energy conservation improvements to the thermal envelope continue, the fraction of energy consumed by the conditioning of air may increase. Air-tightening programs, while decreasing energy requirements, have the tendency to decrease ventilation and its associated energy penalty at the possible expense of adequate indoor air quality. Therefore, more energy may be spent on conditioning air.

Note: This page contains sample records for the topic "forecasts future residential" 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

Advancing Residential Energy Retrofits  

Science Conference Proceedings (OSTI)

To advance the market penetration of residential retrofits, Oak Ridge National Laboratory (ORNL) and Southface Energy Institute (Southface) partnered to provide technical assistance on nine home energy retrofits in metropolitan Atlanta with simulated source energy savings of 30% to 50%. Retrofit measures included duct sealing, air infiltration reductions, attic sealing and roofline insulation, crawlspace sealing, HVAC and water heating equipment replacement, and lighting and appliance upgrades. This paper will present a summary of these measures and their associated impacts on important home performance metrics, such as air infiltration and duct leakage. The average estimated source energy savings for the homes is 33%, and the actual heating season average savings is 32%. Additionally, a case study describing expected and realized energy savings of completed retrofit measures of one of the homes is described in this paper.

Jackson, Roderick K [ORNL; Boudreaux, Philip R [ORNL; Kim, Eyu-Jin [Southface Energy Institute; Roberts, Sydney [Southface Energy Institute

2012-01-01T23:59:59.000Z

462

Load Forecast For use in Resource Adequacy  

E-Print Network (OSTI)

Load Forecast 2019 For use in Resource Adequacy Massoud Jourabchi #12;In today's presentation d l­ Load forecast methodology ­ Drivers of the forecast f i­ Treatment of conservation ­ Incorporating impact of weather ­ Forecast for 2019 #12;Regional Loads (MWA and MW)Regional Loads (MWA and MW

463

Forecast Technical Document Felling and Removals  

E-Print Network (OSTI)

Forecast Technical Document Felling and Removals Forecasts A document describing how volume fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Felling and removals forecasts Background A fellings and removals

464

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity forecast is the combined product of the hard work and expertise of numerous staff members in the Demand the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast. Miguel Garcia

465

Combining forecast weights: Why and how?  

Science Conference Proceedings (OSTI)

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions

Yip Chee Yin; Ng Kok-Haur; Lim Hock-Eam

2012-01-01T23:59:59.000Z

466

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network (OSTI)

1 PROBLEMS OF FORECAST1 Dmitry KUCHARAVY dmitry.kucharavy@insa-strasbourg.fr Roland DE GUIO roland for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods of the forecast errors. Second, using a contradiction analysis, a set of problems related to technology forecast

Paris-Sud XI, Université de

467

Using reforecasts for probabilistic forecast calibration  

E-Print Network (OSTI)

1 Using reforecasts for probabilistic forecast calibration Tom Hamill NOAA Earth System Research that is currently operational. #12;3 Why compute reforecasts? · For many forecast problems, such as long-lead forecasts or high-precipitation events, a few past forecasts may be insufficient for calibrating

Hamill, Tom

468

Assessing Forecast Accuracy Measures Department of Economics  

E-Print Network (OSTI)

Assessing Forecast Accuracy Measures Zhuo Chen Department of Economics Heady Hall 260 Iowa State forecast accuracy measures. In the theoretical direction, for comparing two forecasters, only when the errors are stochastically ordered, the ranking of the forecasts is basically independent of the form

469

Quantitative Precipitation Forecast Techniques for Use in Hydrologic Forecasting  

Science Conference Proceedings (OSTI)

Quantitative hydrologic forecasting usually requires knowledge of the spatial and temporal distribution of precipitation. First, it is important to accurately measure the precipitation falling over a particular watershed of interest. Second, ...

Konstantine P. Georgakakos; Michael D. Hudlow

1984-11-01T23:59:59.000Z

470

Accurate Short Term Load Forecasting for an ESKOM Major Distribution Region in South Africa: An Application of EPRI ANNSTLF  

Science Conference Proceedings (OSTI)

ANNSTLF (Artificial Neural Network Short-term Load Forecaster), developed by EPRI, is a Microsoft Windows-based neural-network load forecasting software that uses historical load and weather parameters to predict future load values. The software requires customization for each utility. This project involved customizing ANNSTLF for the Eastern Region of the South African energy company ESKOM.

2005-09-27T23:59:59.000Z

471

Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model  

E-Print Network (OSTI)

1 1 Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model 2007 Federal prices are being inputted into the model 2 Sales forecast Select yearsSales forecast Select years --Draft 0.53% Irrigation 2.76% Annual Growth Rates Preliminary Electricity ForecastAnnual Growth Rates

472

Can earnings forecasts be improved by taking into account the forecast bias?  

E-Print Network (OSTI)

Can earnings forecasts be improved by taking into account the forecast bias? François DOSSOU allow the calculation of earnings adjusted forecasts, for horizons from 1 to 24 months. We explain variables. From the forecast evaluation statistics viewpoints, the adjusted forecasts make it possible quasi

Paris-Sud XI, Université de

473

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

7 7 Range 10 4 48 Clothes Dryer 359 (2) 4 49 Water Heating Water Heater-Family of 4 40 64 (3) 26 294 Water Heater-Family of 2 40 32 (3) 12 140 Note(s): Source(s): 1) $1.139/therm. 2) Cycles/year. 3) Gallons/day. A.D. Little, EIA-Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case, Sept. 2, 1998, p. 30 for range and clothes dryer; LBNL, Energy Data Sourcebook for the U.S. Residential Sector, LBNL-40297, Sept. 1997, p. 62-67 for water heating; GAMA, Consumers' Directory of Certified Efficiency Ratings for Heating and Water Heating Equipment, Apr. 2002, for water heater capacity; and American Gas Association, Gas Facts 1998, December 1999, www.aga.org for range and clothes dryer consumption. Operating Characteristics of Natural Gas Appliances in the Residential Sector

474

RESIDENTIAL THERMOSTATS: COMFORT CONTROLS IN CALIFORNIA HOMES  

E-Print Network (OSTI)

comfort, and alternative cooling strategies”. Occupants veryNon-Compressor Cooling Alternatives for Reducing Residential

Meier, Alan K.

2008-01-01T23:59:59.000Z

475

Today in Energy - Residential Consumption & Efficiency  

Reports and Publications (EIA)

Short, timely articles with graphs about recent residential consumption and efficiency issues and trends

476

Reducing indoor residential exposures to outdoor pollutants  

E-Print Network (OSTI)

combustion in motor vehicles, electricity generation and industrial processes, as well as residential fireplaces and wood

Sherman, Max H.; Matson, Nance E.

2003-01-01T23:59:59.000Z

477

Residential Census Maps - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Home >>Energy Users > Residential Home Page > Census Maps . U. S. Census Regions and Divisions: Contact: James ...

478

Forecasts for CMB ?- and i-type spectral distortion constraints on the primordial power spectrum on scales 8 < k < 10^4 Mpc^-1 with the future Pixie-like experiments  

E-Print Network (OSTI)

Silk damping at redshifts 1.5 x 10^4 < z < 2 x 10^6 erases CMB anisotropies on scales corresponding to the comoving wavenumbers 8 < k < 10^4 Mpc^-1 (10^5 < \\ell < 10^8). This dissipated energy is gained by the CMB monopole, creating distortions from a blackbody in the CMB spectrum of the \\mu-type and the i-type. We study, using Fisher matrices, the constraints we can get from measurements of these spectral distortions on the primordial power spectrum from future experiments such as Pixie, and how these constraints change as we change the frequency resolution and the sensitivity of the experiment. We show that the additional information in the shape of the $i$-type distortions, in combination with the \\mu-type distortions, allows us to break the degeneracy between the amplitude and the spectral index of the power spectrum on these scales and leads to much tighter constraints. We quantify the information contained in both the \\mu-type distortions and the i-type distortions taking into account the partial degeneracy with the y-type distortions and the temperature of the blackbody part of the CMB. We also calculate the constraints possible on the primordial power spectrum when the spectral distortion information is combined with the CMB anisotropies measured by the WMAP, SPT, ACT and Planck experiments.

Rishi Khatri; Rashid A. Sunyaev

2013-03-28T23:59:59.000Z

479

Downscaling Extended Weather Forecasts for Hydrologic Prediction  

SciTech Connect

Weather and climate forecasts are critical inputs to hydrologic forecasting systems. The National Center for Environmental Prediction (NCEP) issues 8-15 days outlook daily for the U.S. based on the Medium Range Forecast (MRF) model, which is a global model applied at about 2? spatial resolution. Because of the relatively coarse spatial resolution, weather forecasts produced by the MRF model cannot be applied directly to hydrologic forecasting models that require high spatial resolution to represent land surface hydrology. A mesoscale atmospheric model was used to dynamically downscale the 1-8 day extended global weather forecasts to test the feasibility of hydrologic forecasting through this model nesting approach. Atmospheric conditions of each 8-day forecast during the period 1990-2000 were used to provide initial and boundary conditions for the mesoscale model to produce an 8-day atmospheric forecast for the western U.S. at 30 km spatial resolution. To examine the impact of initialization of the land surface state on forecast skill, two sets of simulations were performed with the land surface state initialized based on the global forecasts versus land surface conditions from a continuous mesoscale simulation driven by the NCEP reanalysis. Comparison of the skill of the global and downscaled precipitation forecasts in the western U.S. showed higher skill for the downscaled forecasts at all precipitation thresholds and increasingly larger differences at the larger thresholds. Analyses of the surface temperature forecasts show that the mesoscale forecasts generally reduced the root-mean-square error by about 1.5 C compared to the global forecasts, because of the much better resolved topography at 30 km spatial resolution. In addition, initialization of the land surface states has large impacts on the temperature forecasts, but not the precipitation forecasts. The improvements in forecast skill using downscaling could be potentially significant for improving hydrologic forecasts for managing river basins.

Leung, Lai-Yung R.; Qian, Yun

2005-03-01T23:59:59.000Z

480

Better Buildings Neighborhood Program: Better Buildings Residential  

NLE Websites -- All DOE Office Websites (Extended Search)

Better Better Buildings Residential Network-Current Members to someone by E-mail Share Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on Facebook Tweet about Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on Twitter Bookmark Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on Google Bookmark Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on Delicious Rank Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on Digg Find More places to share Better Buildings Neighborhood Program: Better Buildings Residential Network-Current Members on AddThis.com...

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


481

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":""}]}

482

Value of Wind Power Forecasting  

DOE Green Energy (OSTI)

This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts.

Lew, D.; Milligan, M.; Jordan, G.; Piwko, R.

2011-04-01T23:59:59.000Z

483

Fuzzy forecasting with DNA computing  

Science Conference Proceedings (OSTI)

There are many forecasting techniques including: exponential smoothing, ARIMA model, GARCH model, neural networks and genetic algorithm, etc. Since financial time series may be influenced by many factors, conventional model based techniques and hard ...

Don Jyh-Fu Jeng; Junzo Watada; Berlin Wu; Jui-Yu Wu

2006-06-01T23:59:59.000Z

484

Sampling Errors in Seasonal Forecasting  

Science Conference Proceedings (OSTI)

The limited numbers of start dates and ensemble sizes in seasonal forecasts lead to sampling errors in predictions. Defining the magnitude of these sampling errors would be useful for end users as well as informing decisions on resource ...

Stephen Cusack; Alberto Arribas

2009-03-01T23:59:59.000Z

485

Scoring Rules for Forecast Verification  

Science Conference Proceedings (OSTI)

The problem of probabilistic forecast verification is approached from a theoretical point of view starting from three basic desiderata: additivity, exclusive dependence on physical observations (“locality”), and strictly proper behavior. By ...

Riccardo Benedetti

2010-01-01T23:59:59.000Z

486

Wavelets and Field Forecast Verification  

Science Conference Proceedings (OSTI)

Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous ...

William M. Briggs; Richard A. Levine

1997-06-01T23:59:59.000Z

487

Richardson's Barotropic Forecast: A Reappraisal  

Science Conference Proceedings (OSTI)

To elucidate his numerical technique and to examine the effectiveness of geostrophic initial winds, Lewis Fry Richardson carried out an idealized forecast using the linear shallow-water equations and simple analytical pressure and velocity ...

Peter Lynch

1992-01-01T23:59:59.000Z

488

Winter Residential Heating Oil Prices  

Gasoline and Diesel Fuel Update (EIA)

7 7 Notes: Residential heating oil prices reflect a similar pattern to that shown in spot prices. However, like other retail petroleum prices, they tend to lag changes in wholesale prices in both directions, with the result that they don't rise as rapidly or as much, but they take longer to recede. This chart shows the residential heating oil prices collected under the State Heating Oil and Propane Program (SHOPP), which only runs during the heating season, from October through March. The spike in New York Harbor spot prices last winter carried through to residential prices throughout New England and the Central Atlantic states. Though the spike actually lasted only a few weeks, residential prices ended the heating season well above where they had started.

489

SMUD's Residential Summer Solutions Study  

NLE Websites -- All DOE Office Websites (Extended Search)

SMUD's Residential Summer Solutions Study SMUD's Residential Summer Solutions Study Speaker(s): Karen Herter Date: August 26, 2011 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Janie Page In 2009, the DRRC and SMUD teamed up to test the use of dynamic pricing and communicating thermostats in the small commercial sector. The final results showed summer energy savings of 20%, event impacts of 14%, and bill savings of 25%. In 2011, the same team will conduct a similar study involving residential customers with interval meters. The study is designed to inform the transition to the Sacramento smart grid through experimentation with real-time energy use data and communicating thermostats, both with and without dynamic pricing. Three randomly chosen groups of residential customers were offered one of three equipment configuration treatments: (a)

490

Residential heating oil prices increase  

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

ago to 3.98 per gallon. That's up 6-tenths of a penny from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. Heating oil...

491

Residential heating oil prices increase  

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

last week to 3.92 per gallon. That's down 11 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. The price for...

492

Residential heating oil prices increase  

Gasoline and Diesel Fuel Update (EIA)

last week to 3.96 per gallon. That's down 2.6 cents from a year ago, based on the residential heating fuel survey by the U.S. Energy Information Administration. The price for...

493

Residential Broadband, 2nd edition  

Science Conference Proceedings (OSTI)

From the Publisher:This comprehensive, accessible resource organizes and puts in context the complexities and variables that characterize full-scale deployment of residential broadband networks. This book provides valuable information and perspective ...

George Abe

1999-12-01T23:59:59.000Z

494

The Potential Impact of Using Persistence as a Reference Forecast on Perceived Forecast Skill  

Science Conference Proceedings (OSTI)

Skill is defined as actual forecast performance relative to the performance of a reference forecast. It is shown that the choice of reference (e.g., random or persistence) can affect the perceived performance of the forecast system. Two scores, ...

Marion P. Mittermaier

2008-10-01T23:59:59.000Z

495

The Complex Relationship between Forecast Skill and Forecast Value: A Real-World Analysis  

Science Conference Proceedings (OSTI)

For routine forecasts of temperature and precipitation, the relative skill advantage of human forecasters with respect to the numerical–statistical guidance is small (and diminishing). Since the relationship between forecast skill and the value ...

Paul J. Roebber; Lance F. Bosart

1996-12-01T23:59:59.000Z

496

Evaluation of Wave Forecasts Consistent with Tropical Cyclone Warning Center Wind Forecasts  

Science Conference Proceedings (OSTI)

An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in near–real time to forecasters since summer 2007. The algorithm removes the tropical cyclone from ...

Charles R. Sampson; Paul A. Wittmann; Efren A. Serra; Hendrik L. Tolman; Jessica Schauer; Timothy Marchok

2013-02-01T23:59:59.000Z

497

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

498

Ability to Forecast Regional Soil Moisture with a Distributed Hydrological Model Using ECMWF Rainfall Forecasts  

Science Conference Proceedings (OSTI)

This study mimics an online forecast system to provide nine day-ahead forecasts of regional soil moisture. It uses modified ensemble rainfall forecasts from the numerical weather prediction model of the European Centre for Medium-Range Weather ...

J. M. Schuurmans; M. F. P. Bierkens

2009-04-01T23:59:59.000Z

499

Spatial Structure, Forecast Errors, and Predictability of the South Asian Monsoon in CFS Monthly Retrospective Forecasts  

Science Conference Proceedings (OSTI)

The spatial structure of the boreal summer South Asian monsoon in the ensemble mean of monthly retrospective forecasts by the Climate Forecast System of the National Centers for Environmental Prediction is examined. The forecast errors and ...

Hae-Kyung Lee Drbohlav; V. Krishnamurthy

2010-09-01T23:59:59.000Z

500

What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting  

Science Conference Proceedings (OSTI)

Differences of opinion exist among forecasters—and between forecasters and users—regarding the meaning of the phrase “good (bad) weather forecasts.” These differences of opinion are fueled by a lack of clarity and/or understanding concerning the ...

Allan H. Murphy

1993-06-01T23:59:59.000Z