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Note: This page contains sample records for the topic "residential sector forecast" 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.


1

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...

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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.

12

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

13

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.

14

residential sector key indicators | OpenEI  

Open Energy Info (EERE)

residential sector key indicators residential sector key indicators Dataset Summary Description This dataset is the 2009 United States Residential Sector Key Indicators and Consumption, part of the Source EIA Date Released March 01st, 2009 (5 years ago) Date Updated Unknown Keywords AEO consumption EIA energy residential sector key indicators Data application/vnd.ms-excel icon 2009 Residential Sector Key Indicators and Consumption (xls, 55.3 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment http://www.eia.gov/abouteia/copyrights_reuse.cfm Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote

15

Modeling diffusion of electrical appliances in the residential sector  

E-Print Network (OSTI)

Efficiency Standards in the Residential Electricity Sector.France. USDOE (2001). Residential Energy Consumption Survey,long-term response of residential cooling energy demand to

McNeil, Michael A.

2010-01-01T23:59:59.000Z

16

Residential Sector Demand Module 2000, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

1999-12-01T23:59:59.000Z

17

Residential Sector Demand Module 2004, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2004-02-01T23:59:59.000Z

18

Residential Sector Demand Module 2001, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2000-12-01T23:59:59.000Z

19

Residential Sector Demand Module 2002, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2001-12-01T23:59:59.000Z

20

Residential Sector Demand Module 2005, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2005-04-01T23:59:59.000Z

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


21

Residential Sector Demand Module 2003, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2003-01-01T23:59:59.000Z

22

Residential Sector Demand Module 2008, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2008-10-10T23:59:59.000Z

23

Residential Sector Demand Module 2006, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2006-03-01T23:59:59.000Z

24

Residential Sector Demand Module 2009, Model Documentation  

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.

John H. Cymbalsky

2009-05-01T23:59:59.000Z

25

Residential Sector Demand Module 1999, Model Documentation  

Reports and Publications (EIA)

This is the fifth 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 1999.

John H. Cymbalsky

1998-12-01T23:59:59.000Z

26

Residential Sector Demand Module 2007, Model Documentation  

Reports and Publications (EIA)

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.

John H. Cymbalsky

2007-04-26T23:59:59.000Z

27

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

28

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

38 3.2.1. SDG&E Residential Electric Rates and TheirFootprint of Single-Family Residential New Construction.Solar photovoltaic financing: residential sector deployment,

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

29

Country Review of Energy-Efficiency Financial Incentives in the Residential Sector  

E-Print Network (OSTI)

Financial Incentives in the Residential Sector Stephane deFinancial Incentives in the Residential Sector Stephane desavings achieved in the residential sector. In contrast,

Can, Stephane de la Rue du

2011-01-01T23:59:59.000Z

30

Strategies for Low Carbon Growth In India: Industry and Non Residential Sectors  

E-Print Network (OSTI)

Efficiency Scenario (non-residential sector only) – AssumesIndia: Industry and Non Residential Sectors Jayant Sathaye,and support. The Non Residential sector analysis benefited

Sathaye, Jayant

2011-01-01T23:59:59.000Z

31

Solar Photovoltaic Financing: Residential Sector Deployment  

DOE Green Energy (OSTI)

This report presents the information that homeowners and policy makers need to facilitate PV financing at the residential level. The full range of cash payments, bill savings, and tax incentives is covered, as well as potentially available solar attribute payments. Traditional financing is also compared to innovative solutions, many of which are borrowed from the commercial sector. Together, these mechanisms are critical for making the economic case for a residential PV installation, given its high upfront costs. Unfortunately, these programs are presently limited to select locations around the country. By calling attention to these innovative initiatives, this report aims to help policy makers consider greater adoption of these models to benefit homeowners interested installing a residential PV system.

Coughlin, J.; Cory, K.

2009-03-01T23:59:59.000Z

32

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

33

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta*  

E-Print Network (OSTI)

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta* Department of Economics Bayesian and classical methods to forecast employment for eight sectors of the US economy. In addition-sample period and January 1990 to March 2009 as the out-of- sample horizon, we compare the forecast performance

Ahmad, Sajjad

34

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

35

Residential Sector Demand Module 1997, Model Documentation  

Reports and Publications (EIA)

This is the third edition of the Model Documentation Report: Residential Sector DemandModule of the National Energy Modeling System. It reflects changes made to the moduleover the past year for the Annual Energy Outlook 1997. Since last year, a subroutinewas added to the model which allows technology and fuel switching when space heaters,heat pump air conditioners, water heaters, stoves, and clothes dryers are retired in bothpre-1994 and post-1993 single-family homes. Also, a time-dependant function forcomputing the installed capital cost of equipment in new construction and the retail costof replacement equipment in existing housing was added.

John H. Cymbalsky

1997-01-01T23:59:59.000Z

36

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

37

Figure 58. Residential sector adoption of renewable energy ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 58. Residential sector adoption of renewable energy technologies in two cases, 2005-2040 PV and wind (gigawatts) Heat pump ...

38

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

39

EIA Data: 2011 United States Residential Sector Key Indicators and  

Open Energy Info (EERE)

Residential Sector Key Indicators and Residential Sector Key Indicators and Consumption Dataset Summary Description This dataset is the 2011 United States Residential Sector Key Indicators and Consumption, part of the Annual Energy Outlook that highlights changes in the AEO Reference case projections for key energy topics. Source EIA Date Released December 16th, 2010 (4 years ago) Date Updated Unknown Keywords consumption EIA energy residential sector key indicators Data application/vnd.ms-excel icon Residential Sector Key Indicators and Consumption (xls, 62.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment http://www.eia.gov/abouteia/copyrights_reuse.cfm

40

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

Note: This page contains sample records for the topic "residential sector forecast" 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

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

42

Solar Photovoltaic Financing: Residential Sector Deployment  

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

(a subsidiary of U.S. Bancorp), AFC First Financial Corporation, and Gemstone Lease Management, LLC, announced a residential solar lease program for homeowners who meet certain...

43

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

and 2% of total natural gas usage in the residential sector.ignition systems, which will decrease gas usage andincrease electricity usage for gas ranges. Figure 11.4. Cost

Wenzel, T.P.

2010-01-01T23:59:59.000Z

44

EIA Data: 2011 United States Residential Sector Key Indicators...  

Open Energy Info (EERE)

Residential Sector Key Indicators and Consumption This dataset is the 2011 United...

45

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

46

Electricity savings potentials in the residential sector of Bahrain  

SciTech Connect

Electricity is the major fuel (over 99%) used in the residential, commercial, and industrial sectors in Bahrain. In 1992, the total annual electricity consumption in Bahrain was 3.45 terawatt-hours (TWh), of which 1.95 TWh (56%) was used in the residential sector, 0.89 TWh (26%) in the commercial sector, and 0.59 TWh (17%) in the industrial sector. Agricultural energy consumption was 0.02 TWh (less than 1%) of the total energy use. In Bahrain, most residences are air conditioned with window units. The air-conditioning electricity use is at least 50% of total annual residential use. The contribution of residential AC to the peak power consumption is even more significant, approaching 80% of residential peak power demand. Air-conditioning electricity use in the commercial sector is also significant, about 45% of the annual use and over 60% of peak power demand. This paper presents a cost/benefit analysis of energy-efficient technologies in the residential sector. Technologies studied include: energy-efficient air conditioners, insulating houses, improved infiltration, increasing thermostat settings, efficient refrigerators and freezers, efficient water heaters, efficient clothes washers, and compact fluorescent lights. We conservatively estimate a 32% savings in residential electricity use at an average cost of about 4 fils per kWh. (The subsidized cost of residential electricity is about 12 fils per kWh. 1000 fils = 1 Bahrain Dinar = US$ 2.67). We also discuss major policy options needed for implementation of energy-efficiency technologies.

Akbari, H. [Lawrence Berkeley National Lab., CA (United States); Morsy, M.G.; Al-Baharna, N.S. [Univ. of Bahrain, Manama (Bahrain)

1996-08-01T23:59:59.000Z

47

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

48

The Residential Sector: Changing Markets, Changing Technologies  

Science Conference Proceedings (OSTI)

Residential customers in the U.S. are confronted with markets for home services that continue to change rapidly. Not only are markets for traditional "utilities" such as telecommunications services, energy services, and entertainment services transitioning to competitive choice scenarios, but the technology which customers use in each of these arenas is changing rapidly as well. This report outlines the way that customers are responding to these changing market dynamics, in terms of the way they think ab...

1999-12-02T23:59:59.000Z

49

MISCELLANEOUS ELECTRICITY USE IN THE U.S. RESIDENTIAL SECTOR  

E-Print Network (OSTI)

-2010). Our study has two components: a historical analysis of miscellaneous electricity use (1976- 1995 consumption increased at an annual rate of 4.6%. In 1995, miscellaneous electricity consumption totaled 235LBNL-40295 UC-1600 MISCELLANEOUS ELECTRICITY USE IN THE U.S. RESIDENTIAL SECTOR M. C. Sanchez, J. G

50

Greening the Residential Sector: Efforts to Transform the Homebuilding  

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

Greening the Residential Sector: Efforts to Transform the Homebuilding Greening the Residential Sector: Efforts to Transform the Homebuilding Market Speaker(s): Doug King Date: October 16, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Rich Brown The world is changing- regional and global environmental problems have gained prominence, natural resources are becoming increasingly scarce and expensive, people spend more time indoors, and consumers have higher expectations for comfort than ever before - but the way new homes are built has remained largely stagnant. This is a significant problem, as more than 1.5 million new homes are built each year and the typical home will last anywhere between 50 and 75 years. There are various strategies for driving progress, including upgrades to local building codes and increased minimum

51

Table A4. Residential sector key indicators and consumption  

Gasoline and Diesel Fuel Update (EIA)

3 3 U.S. Energy Information Administration | Annual Energy Outlook 2013 Reference case Table A4. Residential sector key indicators and consumption (quadrillion Btu per year, unless otherwise noted) Energy Information Administration / Annual Energy Outlook 2013 Table A4. Residential sector key indicators and consumption (quadrillion Btu per year, unless otherwise noted) Key indicators and consumption Reference case Annual growth 2011-2040 (percent) 2010 2011 2020 2025 2030 2035 2040 Key indicators Households (millions) Single-family ....................................................... 82.85 83.56 91.25 95.37 99.34 103.03 106.77 0.8% Multifamily ........................................................... 25.78 26.07 29.82 32.05 34.54 37.05 39.53 1.4%

52

Statistical Review of UK Residential Sector Electrical Loads  

E-Print Network (OSTI)

This paper presents a comprehensive statistical review of data obtained from a wide range of literature on the most widely used electrical appliances in the UK residential load sector. It focuses on individual appliances and begins by consideration of the electrical operations performed by the load. This approach allows for the loads to be categorised based on the electrical characteristics, and also provides information on the reactive power characteristics of the load, which is often neglected from standard consumption statistics. This data is particularly important for power system analysis. In addition to this, device ownership statistics and probability distribution functions of power demand are presented for the main residential loads. Although the data presented is primarily intended as a resource for the development of load profiles for power system analysis, it contains a large volume of information which provides a useful database for the wider research community.

Tsagarakis, G; Kiprakis, A E

2013-01-01T23:59:59.000Z

53

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

54

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

1989. Residential End-Use Energy Consumption: A Survey ofCathy R. Zoi. 1986. Unit Energy Consumption of ResidentialResidential Unit Energy Consumption Coefficients, Palo Alto,

Wenzel, T.P.

2010-01-01T23:59:59.000Z

55

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

the predominant residential electricity rate structure. Itresidential electricity customers, over 90%, are on the standard domestic residential (DR) rate,

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

56

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 document serves three purposes. First, it is a reference document providing a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

NONE

1995-03-01T23:59:59.000Z

57

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

Science Conference Proceedings (OSTI)

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

NONE

1997-01-01T23:59:59.000Z

58

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

4 4 Cost of a Generic Quad Used in the Residential Sector ($2010 Billion) (1) Residential 1980 10.45 1981 11.20 1982 11.58 1983 11.85 1984 11.65 1985 11.43 1986 10.90 1987 10.55 1988 10.18 1989 9.98 1990 10.12 1991 9.94 1992 9.78 1993 9.77 1994 9.78 1995 9.44 1996 9.44 1997 9.59 1998 9.23 1999 8.97 2000 9.57 2001 10.24 2002 9.33 2003 10.00 2004 10.32 2005 11.10 2006 11.60 2007 11.61 2008 12.29 2009 11.65 2010 9.98 2011 9.99 2012 9.87 2013 9.77 2014 9.76 2015 9.88 2016 9.85 2017 9.83 2018 9.86 2019 9.88 2020 9.91 2021 10.00 2022 10.09 2023 10.11 2024 10.12 2025 10.09 2026 10.10 2027 10.13 2028 10.11 2029 10.06 2030 10.06 2031 10.13 2032 10.23 2033 10.34 2034 10.45 2035 10.57 Note(s): 1) See Table 1.5.1 for generic quad definition. This table provides the consumer cost of a generic quad in the buildings sector. Use this table to estimate the average consumer cost savings resulting from the savings of a generic (primary) quad in the buildings sector. 2) Price of

59

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

60

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

Note: This page contains sample records for the topic "residential sector forecast" 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

End-use electrification in the residential sector : a general equilibrium analysis of technology advancements  

E-Print Network (OSTI)

The residential sector in the U.S. is responsible for about 20% of the country's primary energy use (EIA, 2011). Studies estimate that efficiency improvements in this sector can reduce household energy consumption by over ...

Madan, Tanvir Singh

2012-01-01T23:59:59.000Z

62

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

J.E. 1986. The LBL Residential Energy Model. LawrenceInc. MEANS. 1992. Residential Cost Data: 11th Annual EditionInstitute. 1989. Residential End-Use Energy Consumption: A

Wenzel, T.P.

2010-01-01T23:59:59.000Z

63

State energy price projections for the residential sector, 1993--1994  

Science Conference Proceedings (OSTI)

The purpose of tills report, State Energy Price Projections for the Residential Sector, 1993--1994, is to provide projections of State-level residential prices for 1993 and 1994 for the following fuels: electricity, natural gas, heating oil, liquefied petroleum gas (LPG), kerosene, and coal. Prices for 1992 are also included for comparison purposes. This report also explains the methodology used to produce estimates and the limitations. This report is provided at the request of the Administration for Children and Families, US Department of Health and Human Services, which provides State grants to assist eligible households in meeting the costs of home energy use for space heating or cooling under the Low Income Home Energy Assistance Program (LIHEAP). Funds for LIHEAP are allocated according to each State`s share of home energy expenditures by low income households, if Congress allocates more than $1.975 billion for LIHEAP. Whenever less than $1.975 billion is allocated for LIHEAP, funds are allocated based on the allotment percentages for fiscal year 1984. This has been the case for the last several years. Each State`s share of the funds above $1.975 billion is determined using a formula based, in part, on the price estimates in this report. Several data sources and factors are used in deriving estimates on each State`s share of home energy expenditures by low-income households. One such factor is State-level residential energy prices. The State-level residential energy price projections presented in this report are derived from a set of forecasting equations estimated for each State, based on annual time series data from the Energy Information Administration`s (EIA) State Energy Price and Expenditure Report (SEPER) database, the EIA Natural Gas Monthly (NGM), the EIA Petroleum Marketing Annual (PMA), and the EIA Electric Power Monthly (EPM).

Not Available

1993-11-01T23:59:59.000Z

64

Modeling diffusion of electrical appliances in the residential sector  

E-Print Network (OSTI)

Regression Results for Appliances Refrigerator Coefficientdiffusion of electrical appliances in the residential sectorfor modeling residential appliance uptake as a function of

McNeil, Michael A.

2010-01-01T23:59:59.000Z

65

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

66

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

67

EIA Energy Efficiency-Residential Sector Energy Intensities, 1978-2001  

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

Residential Sector Energy Intensities Residential Sector Energy Intensities RESIDENTIAL SECTOR ENERGY INTENSITIES: 1978-2005 Released Date: August 2004 Page Last Modified:June 2009 These tables provide estimates of residential sector energy consumption and energy intensities for 1978 -1984, 1987, 1990, 1993, 1997, 2001 and 2005 based on the Residential Energy Consumption Survey (RECS). Total Site Energy Consumption (U.S. and Census Region) Html Excel PDF By Type of Housing Unit (Table 1a) html Table 1a excel table 1a. excel table 1a. Weather-Adjusted by Type of Housing Unit (Table 1b) html table 1b excel table 1b excel table 1b Total Primary Energy Consumption (U.S. and Census Region) By Type of Housing Unit (Table 1c) html Table 1c excel table 1c excel table 1c Weather-Adjusted by Type of Housing Unit (Table 1d)

68

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

69

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Cason. 1990. Residential Energy Usage Comparison Project: AnResearch, Inc. 1985. Energy Usage Analysis of Residentialthere are few data on the energy usage of new buildings,

Wenzel, T.P.

2010-01-01T23:59:59.000Z

70

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

Square footage includes attic, garage, and basement square footage. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008. Share of Average Home Size (1) Average Home Size...

71

Carbon dioxide emissions grow in the residential sector ...  

U.S. Energy Information Administration (EIA)

Accounting for this increased CO 2 share is the 19-fold growth in residential ... illustrates the importance of the relationship of power plant ...

72

Solar Adoption and Energy Consumption in the Residential Sector.  

E-Print Network (OSTI)

??This dissertation analyzes the energy consumption behavior of residential adopters of solar photovoltaic systems (solar-PV). Based on large data sets from the San Diego region… (more)

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

73

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Options for Residential Appliances and Space ConditioningAHAM, Association of Home Appliance Manufacturers. 1991.AHAM, Association of Home Appliance Manufacturers. 1996.

Wenzel, T.P.

2010-01-01T23:59:59.000Z

74

Updated Buildings Sector Appliance and Equipment Costs and Efficiency  

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

Full report (4.1 mb) Full report (4.1 mb) Heating, cooling, & water heating equipment Appendix A - Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case (1.9 mb) Appendix B - Technology Forecast Updates - Residential and Commercial Building Technologies - Advanced Case (1.3 mb) Lighting and commercial ventilation & refrigeration equipment Appendix C - Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case (1.1 mb) Appendix D - Technology Forecast Updates - Residential and Commercial Building Technologies - Advanced Case (1.1 mb) Updated Buildings Sector Appliance and Equipment Costs and Efficiency Release date: August 7, 2013 Energy used in the residential and commercial sectors provides a wide range

75

Major models and data sources for residential and commercial sector energy conservation analysis. Final report  

SciTech Connect

Major models and data sources are reviewed that can be used for energy-conservation analysis in the residential and commercial sectors to provide an introduction to the information that can or is available to DOE in order to further its efforts in analyzing and quantifying their policy and program requirements. Models and data sources examined in the residential sector are: ORNL Residential Energy Model; BECOM; NEPOOL; MATH/CHRDS; NIECS; Energy Consumption Data Base: Household Sector; Patterns of Energy Use by Electrical Appliances Data Base; Annual Housing Survey; 1970 Census of Housing; AIA Research Corporation Data Base; RECS; Solar Market Development Model; and ORNL Buildings Energy Use Data Book. Models and data sources examined in the commercial sector are: ORNL Commercial Sector Model of Energy Demand; BECOM; NEPOOL; Energy Consumption Data Base: Commercial Sector; F.W. Dodge Data Base; NFIB Energy Report for Small Businesses; ADL Commercial Sector Energy Use Data Base; AIA Research Corporation Data Base; Nonresidential Buildings Surveys of Energy Consumption; General Electric Co: Commercial Sector Data Base; The BOMA Commercial Sector Data Base; The Tishman-Syska and Hennessy Data Base; The NEMA Commercial Sector Data Base; ORNL Buildings Energy Use Data Book; and Solar Market Development Model. Purpose; basis for model structure; policy variables and parameters; level of regional, sectoral, and fuels detail; outputs; input requirements; sources of data; computer accessibility and requirements; and a bibliography are provided for each model and data source.

Not Available

1980-09-01T23:59:59.000Z

76

Potential Energy Choices and Their Determinants for the Residential Sector  

Science Conference Proceedings (OSTI)

This report is part of the Understanding Energy Markets research initiative and is the third in a series of four reports championed by EPRIsolutions to deepen the understanding of the residential marketplace. It is designed to support energy suppliers in predicting and winning market share potential. In particular, it examines residential customers' past switching behavior, reasons for switching electricity providers, profiles of likely electric switchers, and interest in bundled energy offers. The exper...

2000-11-22T23:59:59.000Z

77

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

78

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

79

Strategies for Low Carbon Growth In India: Industry and Non Residential Sectors  

Science Conference Proceedings (OSTI)

This report analyzed the potential for increasing energy efficiency and reducing greenhouse gas emissions (GHGs) in the non-residential building and the industrial sectors in India. The first two sections describe the research and analysis supporting the establishment of baseline energy consumption using a bottom up approach for the non residential sector and for the industry sector respectively. The third section covers the explanation of a modeling framework where GHG emissions are projected according to a baseline scenario and alternative scenarios that account for the implementation of cleaner technology.

Sathaye, Jayant; de la Rue du Can, Stephane; Iyer, Maithili; McNeil, Michael; Kramer, Klaas Jan; Roy, Joyashree; Roy, Moumita; Chowdhury, Shreya Roy

2011-04-15T23:59:59.000Z

80

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

to 1,499 24% 1,500 to 1,999 16% 2,000 to 2,499 9% 2,500 to 2,999 7% 3,000 or more 11% Total 100% Source(s): EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table HC1-3....

Note: This page contains sample records for the topic "residential sector forecast" 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

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

6.9% 5 or more units 2.1% 13.0% 15.0% Mobile Homes 5.1% 1.1% 6.2% Total 70.3% 29.6% 100% Source(s): EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table HC3-1 and HC4...

82

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

83

State energy price projections for the residential sector, 1992--1993  

Science Conference Proceedings (OSTI)

The purpose of this report, State Energy Price Projections for the Residential Sector, 1992--1993, is to provide projections of State-level residential prices for 1992 and 1993 for the following fuels: electricity, natural gas, heating oil, liquefied petroleum gas (LPG), kerosene, and coal. Prices for 1991 are also included for comparison purposes. This report also explains the methodology used to produce these estimates and the limitations.

Not Available

1992-09-24T23:59:59.000Z

84

State energy price projections for the residential sector, 1992--1993. [Contains model documentation  

SciTech Connect

The purpose of this report, State Energy Price Projections for the Residential Sector, 1992--1993, is to provide projections of State-level residential prices for 1992 and 1993 for the following fuels: electricity, natural gas, heating oil, liquefied petroleum gas (LPG), kerosene, and coal. Prices for 1991 are also included for comparison purposes. This report also explains the methodology used to produce these estimates and the limitations.

Not Available

1992-09-24T23:59:59.000Z

85

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

1 1 2005 Energy Expenditures per Household, by Housing Type and Square Footage ($2010) Per Household Single-Family 1.16 Detached 1.16 Attached 1.20 Multi-Family 1.66 2 to 4 units 1.90 5 or more units 1.53 Mobile Home 1.76 All Homes 1.12 Note(s): Source(s): 1) Energy expenditures per square foot were calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table US-1 part1; and EIA, Annual Energy Review 2010, Oct. 2011, Appendix D, p. 353 for

86

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

2 2 2005 Household Energy Expenditures, by Vintage ($2010) | Year | Prior to 1950 887 | 22% 1950 to 1969 771 | 22% 1970 to 1979 736 | 16% 1980 to 1989 741 | 16% 1990 to 1999 752 | 16% 2000 to 2005 777 | 9% | Average 780 | Total 100% Note(s): Source(s): 1.24 2,003 1) Energy expenditures per square foot were calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008 for 2005 expenditures; and EIA, Annual Energy Review 2010, Oct. 2011, Appendix D, p. 353 for price inflators.

87

Demand-side Management Strategies and the Residential Sector: Lessons from International Experience  

E-Print Network (OSTI)

in producing a given level of output or activity. It is measured by the quantity of energy required to perform a particular activity (service) expressed as energy per unit of output or activity measure of service (EERE, 2010). In the residential sector...

Haney, Aoife Brophy; Jamasb, Tooraj; Platchkov, Laura M.; Pollitt, Michael G.

88

Buildings Energy Data Book: 1.2 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

Residential Sector Energy Consumption March 2012 1.2.9 Implicit Price Deflators (2005 1.00) Year Year Year 1980 0.48 1990 0.72 2000 0.89 1981 0.52 1991 0.75 2001 0.91 1982 0.55...

89

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

4 4 2005 Average Household Expenditures as Percent of Annual Income, by Census Region ($2010) Item Energy (1) Shelter (2) Food Telephone, water and other public services Household supplies, furnishings and equipment (3) Transportation (4) Healthcare Education Personal taxes (5) Average Annual Expenditures Average Annual Income Note(s): Source(s): 1) Average household energy expenditures are calculated from the Residential Energy Consumption Survey (RECS), while average expenditures for other categories are calculated from the Consumer Expenditure Survey (CE). RECS assumed total US households to be 111,090,617 in 2005, while the CE data is based on 117,356,000 "consumer units," which the Bureau of Labor Statistics defines to be financially independent persons or groups of people that use their incomes to make joint expenditure decisions, including all members of a

90

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

3 3 2005 Average Household Expenditures, by Census Region ($2010) Item Energy (1) Shelter (2) Food Telephone, water and other public services Household supplies, furnishings and equipment (3) Transportation (4) Healthcare Education Personal taxes (5) Other expenditures Average Annual Income Note(s): Source(s): 1) Average household energy expenditures are calculated from the Residential Energy Consumption Survey (RECS), while average expenditures for other categories are calculated from the Consumer Expenditure Survey (CE). RECS assumed total US households to be 111,090,617 in 2005, while the CE data is based on 117,356,000 "consumer units," which the Bureau of Labor Statistics defines to be financially independent persons or groups of people that use their incomes to make joint expenditure decisions, including all members of a

91

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

5 5 2010 Residential Energy End-Use Expenditure Splits, by Fuel Type ($2010 Billion) (1) Natural Petroleum Gas Distil. LPG Kerosene Total Coal Electricity Total Percent Space Heating (2) 38.7 11.2 8.0 19.8 0.0 14.3 72.9 28.9% Space Cooling (3) 0.0 35.4 35.4 14.0% Water Heating (4) 14.3 2.1 2.0 4.0 14.2 32.6 12.9% Lighting 22.6 22.6 9.0% Refrigeration (5) 14.9 14.9 5.9% Electronics (6) 17.8 17.8 7.1% Cooking 2.4 0.8 0.8 6.0 9.2 3.7% Wet Cleaning (7) 0.6 10.7 11.3 4.5% Computers 5.6 5.6 2.2% Other (8) 0.0 4.4 4.4 6.7 11.1 4.4% Adjust to SEDS (9) 13.6 13.6 5.4% Total 56.1 13.3 15.2 29.0 0.0 166.8 251.8 100% Note(s): Source(s): 0.5 0.5 1) Expenditures include coal and exclude wood. 2) Includes furnace fans ($4.5 billion). 3) Fan energy use included. 4) Includes residential recreational water heating ($1.4 billion). 5) Includes refrigerators ($15.3 billion) and freezers ($4.4 billion). 6) Includes color televisions ($11.0

92

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

3 3 Residential Aggregate Energy Expenditures, by Year and Major Fuel Type ($2010 Billion) (1) Electricity Total 1980 158.5 1981 164.0 1982 172.3 1983 176.1 1984 178.5 1985 176.8 1986 169.2 1987 167.1 1988 170.1 1989 172.8 1990 168.2 1991 169.9 1992 166.7 1993 175.6 1994 174.9 1995 172.7 1996 181.8 1997 180.0 1998 173.5 1999 174.0 2000 192.8 2001 203.3 2002 192.1 2003 208.8 2004 215.1 2005 236.7 2006 240.0 2007 246.1 2008 259.6 2009 241.6 2010 251.8 2011 251.3 2012 247.1 2013 240.3 2014 239.4 2015 241.7 2016 241.8 2017 243.0 2018 244.7 2019 246.4 2020 247.9 2021 250.4 2022 253.3 2023 255.6 2024 257.8 2025 260.3 2026 263.2 2027 266.0 2028 267.6 2029 268.1 2030 269.7 2031 272.9 2032 276.6 2033 280.4 2034 284.6 2035 288.6 Note(s): Source(s): 1) Residential petroleum products include distillate fuel oil, LPG, and kerosene. EIA, State Energy Data 2009: Prices and Expenditures, Jun. 2011, Table 2 for 1980-2009; EIA, Annual Energy Outlook 2012 Early Release, Jan. 2012, Table

93

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

SciTech Connect

With the emergence of China as the world's largest energy consumer, the awareness of developing country energy consumption has risen. According to common economic scenarios, the rest of the developing world will probably see an economic expansion as well. With this growth will surely come continued rapid growth in energy demand. This paper explores the dynamics of that demand growth for electricity in the residential sector and the realistic potential for coping with it through efficiency. In 2000, only 66% of developing world households had access to electricity. Appliance ownership rates remain low, but with better access to electricity and a higher income one can expect that households will see their electricity consumption rise significantly. This paper forecasts developing country appliance growth using econometric modeling. Products considered explicitly - refrigerators, air conditioners, lighting, washing machines, fans, televisions, stand-by power, water heating and space heating - represent the bulk of household electricity consumption in developing countries. The resulting diffusion model determines the trend and dynamics of demand growth at a level of detail not accessible by models of a more aggregate nature. In addition, the paper presents scenarios for reducing residential consumption through cost-effective and/or best practice 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, which allows for a realistic assessment of efficiency opportunities at the national or regional level. The past decades have seen some of the developing world moving towards a standard of living previously reserved for industrialized countries. Rapid economic development, combined with large populations has led to first China and now India to emerging as 'energy giants', a phenomenon that is expected to continue, accelerate and spread to other countries. This paper explores the potential for slowing energy consumption and greenhouse gas emissions in the residential sector in developing countries and evaluates the potential of energy savings and emissions mitigation through market transformation programs such as, but not limited to Energy Efficiency Standards and Labeling (EES&L). The bottom-up methodology used allows one to identify which end uses and regions have the greatest potential for savings.

Letschert, Virginie; McNeil, Michael A.

2008-05-13T23:59:59.000Z

94

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

SciTech Connect

With the emergence of China as the world's largest energy consumer, the awareness of developing country energy consumption has risen. According to common economic scenarios, the rest of the developing world will probably see an economic expansion as well. With this growth will surely come continued rapid growth in energy demand. This paper explores the dynamics of that demand growth for electricity in the residential sector and the realistic potential for coping with it through efficiency. In 2000, only 66% of developing world households had access to electricity. Appliance ownership rates remain low, but with better access to electricity and a higher income one can expect that households will see their electricity consumption rise significantly. This paper forecasts developing country appliance growth using econometric modeling. Products considered explicitly - refrigerators, air conditioners, lighting, washing machines, fans, televisions, stand-by power, water heating and space heating - represent the bulk of household electricity consumption in developing countries. The resulting diffusion model determines the trend and dynamics of demand growth at a level of detail not accessible by models of a more aggregate nature. In addition, the paper presents scenarios for reducing residential consumption through cost-effective and/or best practice 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, which allows for a realistic assessment of efficiency opportunities at the national or regional level. The past decades have seen some of the developing world moving towards a standard of living previously reserved for industrialized countries. Rapid economic development, combined with large populations has led to first China and now India to emerging as 'energy giants', a phenomenon that is expected to continue, accelerate and spread to other countries. This paper explores the potential for slowing energy consumption and greenhouse gas emissions in the residential sector in developing countries and evaluates the potential of energy savings and emissions mitigation through market transformation programs such as, but not limited to Energy Efficiency Standards and Labeling (EES&L). The bottom-up methodology used allows one to identify which end uses and regions have the greatest potential for savings.

Letschert, Virginie; McNeil, Michael A.

2008-05-13T23:59:59.000Z

95

A supply forecasting model for Zimbabwe's corn sector: a time series and structural analysis  

E-Print Network (OSTI)

The Zimbabwean government utilizes the corn supply forecasts to establish producer prices for the following growing season, estimate corn storage and handling costs, project corn import needs and associated costs, and to assess the Grain Marketing Board's financial resource needs. Thus, the corn supply forecasts are important information used by the government for contingency planning, decision-making, policy-formulation and implementation. As such, the need for accurate forecasts is obvious. The objectives of the study are: (a) determine how changes in the government-established producer price affects the quantity of corn supplied to the Grain Marketing Board by the large-scale corn-producing sector and (b) whether including rainfall or rainfall probabilities into econometric models would result in an improvement of corn supply forecasts compared to current forecasts by the government. In order to accomplish the first objective a supply elasticity model was specified and estimated using ordinary least squares. This model is intended to provide 'de insight to the government regarding the influence of the government-established corn price and other related variables on corn supplied to the Grain Marketing Board by the large-scale producers. Thus, the estimated model would be useful to the government when establishing corn prices in March/April for production in the following growing season (October - February). To achieve the second objective, preliminary analysis was carried out to verify whether there is statistical evidence to support the hypothesis that rainfall cause" corn production and supply, and also corn prices and sales. Specifically the preliminary analysis involved using the Granger causality tests, stationarity tests and innovation accounting (impulse responses and forecast error decomposition). Having verified and quantified the causal effects of rainfall on corn production and supply, the next task was to investigate whether including rainfall and/or drought probabilities into forecasting econometric models would help provide improved out-of-sample forecasts compared to the government's forecasts. The forecasting accuracy of the models (short-run) was evaluated using standard statistical measures such as, the mean square error (MSE), mean absolute percentage error (MAPEI), improved mean absolute percentage error (IMAPE) and Theil's U-statistic, and thereupon select the best model. The results indicated that by incorporating rainfall and/or rainfall probabilities into econometric forecasting models, there was substantial improvement in corn supply forecasts. It follows that the the government would likely find it beneficial to incorporate the rainfall variable into their forecasting effort.

Makaudze, Ephias

1993-01-01T23:59:59.000Z

96

Directory of energy efficiency information services for the residential and commercial sectors  

SciTech Connect

This directory is a compilation of organizations which disseminate a wide range of information on the efficient use of energy in the residential and commercial sectors. Each organization's services are defined by the informations' targeted audience, types of services offered, topics and sectors addressed and access terms required. The organizations included in this directory are based on the Guide to Energy Efficiency Information Services for the Residential and Commercial Sectors, June 1987. The information is presented in two formats in this directory, each focusing on different manners of data retrieval. Section One provides a matrix illustrating the information available by the type of energy-efficiency services offered and Section Two presents information on available services in an alphabetized list by the organization name.

Not Available

1988-11-30T23:59:59.000Z

97

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

5 5 2005 Households and Energy Expenditures, by Income Level ($2010) Energy Expenditures by Household Income Households (millions) Household Less than $10,000 9.9 9% $10,000 to $14,999 8.5 8% $15,000 to $19,999 8.4 8% $20,000 to $29,999 15.1 14% $30,000 to $39,999 13.6 12% $40,000 to $49,999 11.0 10% $50,000 to $74,999 19.8 18% $75,000 to $99,999 10.6 10% $100,000 or more 14.2 13% Total 111.1 100% Note(s): Source(s): 7% 1) See Table 2.3.15 for more on energy burdens. 2) A household is defined as a family, an individual, or a group of up to nine unrelated individuals occupying the same housing unit. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table US-1 part 2; and EIA, Annual Energy Review 2010, Oct. 2011, Appendix D, p. 353 for price inflators. 2,431 847 3% 2,774 909 3% 1,995

98

Energy-Efficient Water Heating Program for the Residential Sector.  

Science Conference Proceedings (OSTI)

During the power surplus period of the late 1980's, Bonneville sponsored market research which provided an understanding of the market environment in the water heating end-use. The major areas of investigation included market trends, consumer purchasing practices, unit price, and availability of energy-efficient models. In 1988, Bonneville conducted a series of meetings with utilities operating water heater programs. Discussions focused on utility program concerns and the appropriate role for Bonneville as the region seeks efficiency in residential water heating. The design of the Program is based to a large degree on the experiences gained by regional utilities operating water heater incentive programs. In addition, an analysis of incentive programs operated outside the region has been helpful in the development of a regional program. Bonneville is a member of the Appliance Efficiency Group (AEG), formerly the Northwest Appliance Efficiency Group, and participates in discussions on water heating issues as they relate to the Pacific Northwest. The work done with the Appliance Efficiency Group has provided additional input in the development of the Program. This Program has been developed using a Public Involvement Process. A draft program strategy was made available to the public for comment during April 1990. The comments received were considered in the development of this document.

United States. Bonneville Power Administration.

1990-09-01T23:59:59.000Z

99

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

8 8 2035 Residential Energy End-Use Expenditure Splits, by Fuel Type ($2010 Billion) (1) Natural Petroleum Gas Distil. LPG Kerosene Total Coal Electricity Total Percent Space Heating (2) 44.3 10.3 7.7 18.6 0.0 16.0 79.0 27.4% Space Cooling (3) 0.0 40.6 40.6 14.1% Water Heating 17.6 1.2 1.2 2.3 17.7 37.6 13.0% Lighting 15.5 15.5 5.4% Refrigeration (4) 17.0 17.0 5.9% Electronics (5) 14.2 14.2 4.9% Wet Cleaning (6) 0.9 10.4 11.3 3.9% Cooking 3.2 0.8 0.8 4.8 8.9 3.1% Computers 8.7 8.7 3.0% Other (7) 0.0 7.7 7.7 47.9 55.7 19.3% Total 66.0 11.5 17.5 29.6 0.0 193.0 288.6 100% Note(s): Source(s): 0.6 0.6 1) Expenditures include coal and exclude wood. 2) Includes furnace fans ($4.8 billion). 3) Fan energy use included. 4) Includes refrigerators ($14.1 billion) and freezers ($2.9 billion). 5) Includes color televisions ($14.2 billion). 6) Includes clothes washers ($0.8 billion), natural gas

100

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

2 2 Residential Energy Prices, by Year and Fuel Type ($2010) LPG ($/gal) 1980 2.24 1981 2.51 1982 2.30 1983 2.14 1984 2.10 1985 1.96 1986 1.54 1987 1.42 1988 1.39 1989 1.48 1990 1.69 1991 1.56 1992 1.40 1993 1.33 1994 1.27 1995 1.22 1996 1.37 1997 1.34 1998 1.15 1999 1.16 2000 1.70 2001 1.59 2002 1.42 2003 1.67 2004 1.84 2005 2.36 2006 2.64 2007 2.81 2008 3.41 2009 2.52 2010 2.92 2011 3.62 2012 3.65 2013 3.43 2014 3.60 2015 3.74 2016 3.79 2017 3.86 2018 3.89 2019 3.92 2020 3.96 2021 3.99 2022 4.02 2023 4.07 2024 4.10 2025 4.15 2026 4.19 2027 4.23 2028 4.26 2029 4.30 2030 4.34 2031 4.35 2032 4.38 2033 4.43 2034 4.50 2035 4.55 Source(s): EIA, State Energy Data 2009: Prices and Expenditures, Jun. 2011, Table 2, p. 24-25 for 1980-2009; EIA, Annual Energy Outlook 2012 Early Release, Jan. 2012, Table A3, p. 6-8 for 2010-2035 and Table G1, p. 215 for fuels' heat content; and EIA, Annual Energy Review 2010, Oct. 2011, Appendix D, p. 353 for

Note: This page contains sample records for the topic "residential sector forecast" 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

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

7 7 2025 Residential Energy End-Use Expenditure Splits, by Fuel Type ($2010 Billion) (1) Natural Petroleum Gas Distil. LPG Kerosene Total Coal Electricity Total Percent Space Heating (2) 39.7 11.5 7.8 19.9 0.0 15.0 74.5 28.6% Space Cooling (3) 0.0 36.2 36.2 13.9% Water Heating 16.0 1.4 1.3 2.7 17.1 35.9 13.8% Lighting 15.2 15.2 5.8% Refrigeration (4) 15.5 15.5 6.0% Electronics (5) 12.0 12.0 4.6% Wet Cleaning (6) 0.8 9.8 10.5 4.1% Cooking 2.7 0.8 0.8 4.3 7.8 3.0% Computers 7.7 7.7 2.9% Other (7) 0.0 6.4 6.4 38.7 45.0 17.3% Total 59.1 12.9 16.3 29.8 0.0 171.3 260.3 100% Note(s): Source(s): 0.6 0.6 1) Expenditures include coal and exclude wood. 2) Includes furnace fans ($4.7 billion). 3) Fan energy use included. 4) Includes refrigerators ($12.7 billion) and freezers ($2.8 billion). 5) Includes color televisions ($12 billion). 6) Includes clothes washers ($0.8 billion), natural gas

102

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

6 6 2015 Residential Energy End-Use Expenditure Splits, by Fuel Type ($2010 Billion) (1) Natural Petroleum Gas Distil. LPG Kerosene Total Coal Electricity Total Percent Space Heating (2) 35.0 13.0 8.1 21.6 0.0 14.0 70.6 29.2% Space Cooling (3) 0.0 33.8 33.8 14.0% Water Heating 13.5 1.9 1.5 3.4 15.8 32.7 13.5% Lighting 17.6 17.6 7.3% Refrigeration (4) 15.0 15.0 6.2% Electronics (5) 10.9 10.9 4.5% Wet Cleaning (6) 0.6 10.8 11.4 4.7% Cooking 2.2 0.9 0.9 3.8 6.8 2.8% Computers 6.3 6.3 2.6% Other (7) 0.0 5.2 5.2 31.3 36.5 15.1% Total 51.3 14.9 15.7 31.1 0.0 159.3 241.7 100% Note(s): Source(s): 0.6 0.6 1) Expenditures include coal and exclude wood. 2) Includes furnace fans ($4.6 billion). 3) Fan energy use included. 4) Includes refrigerators ($12.3 billion) and freezers ($2.8 billion). 5) Includes color televisions ($10.9 billion). 6) Includes clothes washers ($1.1 billion), natural gas

103

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

Residential Energy Prices, by Year and Major Fuel Type ($2010 per Million Btu) Electricity Natural Gas Petroleum (1) Avg. 1980 36.40 8.35 16.77 17.64 1981 38.50 8.88 18.35 19.09 1982 40.15 10.08 17.28 19.98 1983 40.43 11.30 16.08 21.00 1984 38.80 11.02 15.61 20.20 1985 38.92 10.68 14.61 20.10 1986 38.24 9.98 11.88 19.38 1987 37.29 9.22 11.23 18.73 1988 36.22 8.80 10.83 18.02 1989 35.67 8.71 11.96 17.93 1990 35.19 8.63 13.27 18.64 1991 34.88 8.38 12.49 18.31 1992 34.79 8.28 11.23 17.76 1993 34.52 8.47 10.75 17.76 1994 34.04 8.63 10.63 17.87 1995 33.43 8.00 10.33 17.50 1996 32.63 8.21 11.70 17.28 1997 32.34 8.83 11.47 17.69 1998 31.33 8.55 9.96 17.73 1999 30.52 8.29 10.13 17.09 2000 30.13 9.54 14.18 18.06 2001 30.71 11.50 13.98 19.38 2002 29.73 9.24 12.26 17.89 2003 30.05 10.87 14.21 18.88 2004 29.98 11.97 15.54 19.76 2005 30.64 13.66 18.93 21.50 2006 32.67 14.30 21.06 23.34 2007 32.50

104

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

3 3 Share of Total U.S. Households, by Census Region, Division, and Vintage, as of 2005 Prior to 1950 to 1970 to 1980 to 1990 to 2000 to Region 1950 1969 1979 1989 1999 2005 Northeast 6.7% 5.2% 2.4% 2.1% 1.3% 0.8% 18.5% New England 2.1% 1.2% 0.5% 0.5% 0.3% 0.3% 4.9% Middle Atlantic 4.6% 4.0% 1.9% 1.6% 1.0% 0.5% 13.6% Midwest 5.7% 5.8% 3.6% 2.5% 3.7% 1.7% 23.0% East North Central 4.3% 3.9% 2.7% 1.8% 2.1% 1.1% 16.0% West North Central 1.4% 1.9% 0.9% 0.7% 1.6% 0.6% 7.1% South 4.0% 6.9% 6.4% 7.5% 7.5% 4.3% 36.6% South Atlantic 2.0% 3.4% 3.5% 4.2% 4.3% 2.2% 17.4% East South Central 0.9% 1.3% 0.9% 1.0% 1.3% 0.7% 6.2% West South Central 1.2% 2.3% 4.7% 2.2% 1.8% 1.4% 13.6% West 3.4% 4.6% 4.5% 4.6% 3.1% 1.5% 21.8% Mountain 0.7% 1.2% 1.3% 1.5% 1.3% 0.9% 6.8% Pacific 2.8% 3.4% 3.3% 3.1% 1.8% 0.6% 15.0% United States 19.9% 22.5% 17.0% 16.7% 15.6% 8.3% 100% Source(s): All Vintages EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table HC10

105

State energy price projections for the residential sector, 1990--1991  

Science Conference Proceedings (OSTI)

This report was prepared by the Energy Information Administration, Office of Energy Markets and End Use. This service report was developed in response to a request from the Family Support Administration of the US Department of Health and Human Services. Forecasts of State-level residential energy prices for 1990 and 1991 were required as input for the Low-Income Home Energy Assistance Program. This program allocates funds to the States to provide assistance to low-income households in meeting energy cost. (VC)

Not Available

1991-01-18T23:59:59.000Z

106

Regional comparisons of on-site solar potential in the residential and industrial sectors  

SciTech Connect

Regional and sub-regional differences in the potential development of decentralized solar technologies are studied. Two sectors of the economy were selected for intensive analysis: the residential and industrial sectors. In both investigations, the sequence of analysis follows the same general steps: (1) selection of appropriate prototypes within each land-use sector disaggregated by census region; (2) characterization of the end-use energy demand of each prototype in order to match an appropriate decentralized solar technology to the energy demand; (3) assessment of the energy conservation potential within each prototype limited by land use patterns, technology efficiency, and variation in solar insolation; and (4) evaluation of the regional and sub-regional differences in the land use implications of decentralized energy supply technologies that result from the combination of energy demand, energy supply potential, and the subsequent addition of increasingly more restrictive policies to increase the percent contribution of on-site solar energy. Results are presented and discussed. It is concluded that determining regional variations in solar energy contribution for both the residential and industrial sectors appears to be more dependent upon a characterization of existing demand and conservation potential than regional variations in solar insolation. Local governmental decisions influencing developing land use patterns can significantly promote solar energy use and reduce reliance on non-renewable energy sources. These decisions include such measures as solar access protection through controls on vegetation and on building height and density in the residential sector, and district heating systems and industrial co-location in the manufacturing sector. (WHK)

Gatzke, A.E.; Skewes-Cox, A.O.

1980-10-01T23:59:59.000Z

107

LBL-40297 UC-1600 ENERGY DATA SOURCEBOOK FOR THE U.S. RESIDENTIAL SECTOR  

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

40297 40297 UC-1600 ENERGY DATA SOURCEBOOK FOR THE U.S. RESIDENTIAL SECTOR Tom P. Wenzel, Jonathan G. Koomey, Gregory J. Rosenquist, Marla Sanchez, and James W. Hanford September 1997 Energy Analysis Program Environmental Energy Technologies Division Lawrence Berkeley National Laboratory University of California Berkeley, CA 94720 http://enduse.lbl.gov/Projects/RED.html This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State, and Community Programs of the U.S. Department of Energy under Contract No. DE-AC03- 76SF00098. i 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

108

Coal supply/demand, 1980 to 2000. Task 3. Resource applications industrialization system data base. Final review draft. [USA; forecasting 1980 to 2000; sector and regional analysis  

SciTech Connect

This report is a compilation of data and forecasts resulting from an analysis of the coal market and the factors influencing supply and demand. The analyses performed for the forecasts were made on an end-use-sector basis. The sectors analyzed are electric utility, industry demand for steam coal, industry demand for metallurgical coal, residential/commercial, coal demand for synfuel production, and exports. The purpose is to provide coal production and consumption forecasts that can be used to perform detailed, railroad company-specific coal transportation analyses. To make the data applicable for the subsequent transportation analyses, the forecasts have been made for each end-use sector on a regional basis. The supply regions are: Appalachia, East Interior, West Interior and Gulf, Northern Great Plains, and Mountain. The demand regions are the same as the nine Census Bureau regions. Coal production and consumption in the United States are projected to increase dramatically in the next 20 years due to increasing requirements for energy and the unavailability of other sources of energy to supply a substantial portion of this increase. Coal comprises 85 percent of the US recoverable fossil energy reserves and could be mined to supply the increasing energy demands of the US. The NTPSC study found that the additional traffic demands by 1985 may be met by the railways by the way of improved signalization, shorter block sections, centralized traffic control, and other modernization methods without providing for heavy line capacity works. But by 2000 the incremental traffic on some of the major corridors was projected to increase very significantly and is likely to call for special line capacity works involving heavy investment.

Fournier, W.M.; Hasson, V.

1980-10-10T23:59:59.000Z

109

Residential Sector  

Gasoline and Diesel Fuel Update (EIA)

5.94 5.94 15.88 15.47 15.61 15.61 16.19 16.30 16.49 16.10 16.51 16.45 16.78 15.71 16.14 16.45 Middle Atlantic ............. 14.85 15.35 15.64 15.16 15.08 15.70 16.48 15.74 15.27 16.00 16.67 15.96 15.27 15.77 15.99 E. N. Central ................ 11.72 12.37 12.12 12.00 11.48 12.45 12.30 12.03 11.80 12.69 12.68 12.19 12.05 12.05 12.33 W. N. Central .............. 9.64 11.03 11.45 10.12 9.94 11.39 12.05 10.27 10.28 11.56 11.99 10.51 10.59 10.91 11.08 S. Atlantic .................... 11.07 11.48 11.65 11.22 10.89 11.48 11.77 11.31 10.99 11.64 11.85 11.42 11.38 11.38 11.49 E. S. Central ................ 10.05 10.44 10.38 10.41 10.04 10.69 10.65 10.45 10.35 10.96 11.01 10.73 10.32 10.45 10.76 W. S. Central ............... 10.14 10.30 10.35 10.37 10.23 10.94 10.91 10.73 10.59 10.97 11.07 10.80 10.30 10.73 10.88 Mountain .....................

110

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

111

Is Efficiency Enough? Towards a New Framework for Carbon Savings in the California Residential Sector  

E-Print Network (OSTI)

Report. 2004. California Residential Appliance SaturationAdministration (EIA). 1996. Residential Energy ConsumptionEIA). 1999. “A Look at Residential Energy Consumption in

Moezzi, Mithra; Diamond, Rick

2005-01-01T23:59:59.000Z

112

Space-Heating energy used by households in the residential sector.  

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

Detailed Tables Detailed Tables Energy End Uses Ranked by Energy Consumption, 1989 The following 28 tables present detailed data describing the consumption of and expenditures for energy used by households in the residential sector. The data are presented at the national level, Census region and division levels, for climate zones and for the most populous States, as well as for other selected characteristics of households. This section provides assistance in reading the tables by explaining some of the headings for the categories of data. It also explains the use of the row and column factors to compute the relative standard error of the estimates given in the tables. Organization of the Tables The tables cover consumption and expenditures for six topical areas: Major Energy Source

113

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

114

Climate Variability over the Tropical Indian Ocean Sector in the NSIPP Seasonal Forecast System  

Science Conference Proceedings (OSTI)

Prospects for forecasting Indian dipole mode (IDM) events with lead times of a season or more are examined using the NASA Seasonal-to-Interannual Prediction Project (NSIPP) coupled-model forecast system. The mean climatology of the system over ...

Roxana C. Wajsowicz

2004-12-01T23:59:59.000Z

115

Is Efficiency Enough? Towards a New Framework for Carbon Savings in the California Residential Sector  

E-Print Network (OSTI)

only 27% of national energy consumption is consumed directlynational policies affecting the state. Nationwide, energy consumptionNational Association of Home Builders Personal Computer Portland Gas and Electric Residential Appliance Saturation Survey Residential Energy Consumption

Moezzi, Mithra; Diamond, Rick

2005-01-01T23:59:59.000Z

116

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.

117

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

118

Assessment of IP Addressable Microprocessor-Based Adjustable Speed Drives for Small Motors in the Residential Sector Applications  

Science Conference Proceedings (OSTI)

This technical update explores use of microprocessor-based adjustable speed drives (ASDs) used in the residential sector for small motor applications. It provides a detailed summary of the key players in the industry who are involved with the motor control design. It also provides insights about advantages of going from traditional motor control to embedded microprocessor-based electric motor drive systems. Finally, this technical updates describes the possibility of connecting these devices to the Inter...

2008-12-16T23:59:59.000Z

119

Is Efficiency Enough? Towards a New Framework for Carbon Savings in the California Residential Sector  

E-Print Network (OSTI)

of residential electricity consumption surpassed the rate ofresidential electricity consumption increased at a rate ofresidential electricity consumption grew 49%, a slightly lower growth rate

Moezzi, Mithra; Diamond, Rick

2005-01-01T23:59:59.000Z

120

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

Residential Building Component Loads as of 1998 (1) 1) "Load" represents the thermal energy lossesgains that when combined will be offset by a building's heatingcooling system...

Note: This page contains sample records for the topic "residential sector forecast" 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

Potential Impact of Adopting Maximum Technologies as Minimum Efficiency Performance Standards in the U.S. Residential Sector  

SciTech Connect

The US Department of Energy (US DOE) has placed lighting and appliance standards at a very high priority of the U.S. energy policy. However, the maximum energy savings and CO2 emissions reduction achievable via minimum efficiency performance standards (MEPS) has not yet been fully characterized. The Bottom Up Energy Analysis System (BUENAS), first developed in 2007, is a global, generic, and modular tool designed to provide policy makers with estimates of potential impacts resulting from MEPS for a variety of products, at the international and/or regional level. Using the BUENAS framework, we estimated potential national energy savings and CO2 emissions mitigation in the US residential sector that would result from the most aggressive policy foreseeable: standards effective in 2014 set at the current maximum technology (Max Tech) available on the market. This represents the most likely characterization of what can be maximally achieved through MEPS in the US. The authors rely on the latest Technical Support Documents and Analytical Tools published by the U.S. Department of Energy as a source to determine appliance stock turnover and projected efficiency scenarios of what would occur in the absence of policy. In our analysis, national impacts are determined for the following end uses: lighting, television, refrigerator-freezers, central air conditioning, room air conditioning, residential furnaces, and water heating. The analyzed end uses cover approximately 65percent of site energy consumption in the residential sector (50percent of the electricity consumption and 80percent of the natural gas and LPG consumption). This paper uses this BUENAS methodology to calculate that energy savings from Max Tech for the U.S. residential sector products covered in this paper will reach an 18percent reduction in electricity demand compared to the base case and 11percent in Natural Gas and LPG consumption by 2030 The methodology results in reductions in CO2 emissions of a similar magnitude.

Letschert, Virginie; Desroches, Louis-Benoit; McNeil, Michael; Saheb, Yamina

2010-05-03T23:59:59.000Z

122

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.

123

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

124

Is Efficiency Enough? Towards a New Framework for Carbon Savings in the California Residential Sector  

E-Print Network (OSTI)

these years, residential electricity consumption grew 49%, aElectricity consumption has increased over the past 20 years,electricity consumption for single-family residences (7,105 kWh/year).

Moezzi, Mithra; Diamond, Rick

2005-01-01T23:59:59.000Z

125

Achieving real transparency : optimizing building energy ratings and disclosure in the U.S. residential sector  

E-Print Network (OSTI)

Residential energy efficiency in the U.S. has the potential to generate significant energy, carbon, and financial savings. Nonetheless, the market of home energy upgrades remains fragmented, and the number of homes being ...

Nadkarni, Nikhil S. (Nikhil Sunil)

2012-01-01T23:59:59.000Z

126

Potential Impact of Adopting Maximum Technologies as Minimum Efficiency Performance Standards in the U.S. Residential Sector  

E-Print Network (OSTI)

appliance_standards/residential/heating_p roducts_fr_appliance_standards/residential/cac_heatp umps_new_buildings/appliance_standards/residential/fb_tsd_09 07.html

Letschert, Virginie

2010-01-01T23:59:59.000Z

127

Application analysis of solar total energy systems to the residential sector. Volume II, energy requirements. Final report  

DOE Green Energy (OSTI)

This project analyzed the application of solar total energy systems to appropriate segments of the residential sector and determined their market penetration potential. This volume covers the work done on energy requirements definition and includes the following: (1) identification of the single-family and multi-family market segments; (2) regionalization of the United States; (3) electrical and thermal load requirements, including time-dependent profiles; (4) effect of conservation measures on energy requirements; and (5) verification of simulated load data with real data.

Not Available

1979-07-01T23:59:59.000Z

128

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

4 4 Ownership (1) Owned 54.9 104.5 40.3 78% Rented 77.4 71.7 28.4 22% Public Housing 75.7 62.7 28.7 2% Not Public Housing 77.7 73.0 28.4 19% 100% Note(s): Source(s): 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008 2005 Residential Delivered Energy Consumption Intensities, by Ownership of Unit Per Square Per Household Per Household Percent of Foot (thousand Btu) (million Btu) Members (million Btu) Total Consumption

129

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

130

Is Efficiency Enough? Towards a New Framework for Carbon Savings in the California Residential Sector  

E-Print Network (OSTI)

STAT-ABS/Sa_home.htm. California Energy Circuit. August 13,to Surge in Inland Empire. ” California Energy Commission.2002. California Energy Consumption by Sector.

Moezzi, Mithra; Diamond, Rick

2005-01-01T23:59:59.000Z

131

Building-Integrated Photovoltaics (BIPV) in the Residential Sector: An Analysis of Installed Rooftop System Prices  

DOE Green Energy (OSTI)

For more than 30 years, there have been strong efforts to accelerate the deployment of solar-electric systems by developing photovoltaic (PV) products that are fully integrated with building materials. This report examines the status of building-integrated PV (BIPV), with a focus on the cost drivers of residential rooftop systems, and explores key opportunities and challenges in the marketplace.

James, T.; Goodrich, A.; Woodhouse, M.; Margolis, R.; Ong, S.

2011-11-01T23:59:59.000Z

132

Renewable energy options in Saudi Arabia: the economic viability of solar photovoltaics within the residential sector  

Science Conference Proceedings (OSTI)

Renewable energy options, including solar power, are becoming progressively more viable and thus increasingly pose challenges to conventional sources of energy, such as oil, coal and natural gas. Solar Photovoltaic technology is one type of solar energy ... Keywords: Saudi Arabia, feasibility study, renewable energy, residential buildings, solar photovoltaics

Yasser Al-Saleh; Hanan Taleb

2009-02-01T23:59:59.000Z

133

State energy price projections for the residential sector, 1991--1992  

Science Conference Proceedings (OSTI)

The purpose of this report is to provide projections of State-level residential prices for 1991 and 1992 for the following fuels: electricity, natural gas, heating oil, liquefied petroleum gas (LPG), kerosene, and coal. Prices for 1990 are also included for comparison purposes. This report also explains the methodology used to produce these estimates and the limitations. (VC)

Not Available

1991-11-08T23:59:59.000Z

134

1996-2004 Trends in the Single-Family Housing Market: Spatial Analysis of the Residential Sector  

SciTech Connect

This report provides a detailed geographic analysis of two specific topics affecting the residential sector. First, we performed an analysis of new construction market trends using annual building permit data. We report summarized tables and national maps to help illustrate market conditions. Second, we performed a detailed geographic analysis of the housing finance market. We analyzed mortgage application data to provide citable statistics and detailed geographic summarization of the residential housing picture in the US for each year in the 1996-2004 period. The databases were linked to geographic information system tools to provide various map series detailing the results geographically. Looking at these results geographically may suggest potential new markets for TD programs addressing the residential sector that have not been considered previously. For example, we show which lenders affect which regions and which income or mortgage product classes. These results also highlight the issue of housing affordability. Energy efficiency R&D programs focused on developing new technology for the residential sector must be conscious of the costs of products resulting from research that will eventually impact the home owner or new home buyer. Results indicate that home values as a proportion of median family income in Building America communities are closely aligned with the national average of home value as a proportion of median income. Other key findings: • The share of home building and home buying activity continues to rise steadily in the Hot-Dry and Hot-Humid climate zones, while the Mixed-Humid and Cold climate zone shares continue to decline. Other zones remain relatively stable in terms of share of housing activity. • The proportion of home buyers having three times the median family income for their geography has been steadily increasing during the study period. • Growth in the Hispanic/Latino population and to a lesser degree in the Asian population has translated into proportional increases in share of home purchasing by both groups. White home buyers continue to decline as a proportion all home buyers. • Low interest rate climate resulted in lenders moving back to conventional financing, as opposed to government-backed financing, for cases that would be harder to financing in higher rate environments. Government loan products are one mechanism for affecting energy efficiency gains in the residential sector. • The rate environment and concurrent deregulation of the finance industry resulted unprecedented merger and acquisition activity among financial institutions during the study period. This study conducted a thorough accounting of this merger activity to inform the market share analysis provided. • The home finance industry quartiles feature 5 lenders making up the first quartile of home purchase loans, 18 lenders making up the second quartile, 111 lenders making up the third quartile, and the remaining nearly 8,000 lenders make up the fourth quartile.

Anderson, Dave M.; Elliott, Douglas B.

2006-09-05T23:59:59.000Z

135

Comparative Attitudes Toward Utility Type Services: Tracking Perceptions in the Residential Sector  

Science Conference Proceedings (OSTI)

This report is the first in a series of three EPRIsolutions research reports tracking customer perceptions toward five utility providers (electric, natural gas, cable TV, long distance telephone and local telephone), and comparing the performance trends ratings among these different utility companies over time. This particular market assessment focuses on the attitudes of the Residential segment. The next two reports will assess customer attitudes for the large business and mass market segments. The Unde...

2000-10-31T23:59:59.000Z

136

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

137

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

1 1 Type (1) Single-Family: 55.4 106.6 39.4 80.5% Detached 55.0 108.4 39.8 73.9% Attached 60.5 89.3 36.1 6.6% Multi-Family: 78.3 64.1 29.7 14.9% 2 to 4 units 94.3 85.0 35.2 6.3% 5 or more units 69.8 54.4 26.7 8.6% Mobile Homes 74.6 70.4 28.5 4.6% All Housing Types 58.7 95.0 37.0 100% Note(s): Source(s): 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008. 2005 Residential Delivered Energy Consumption Intensities, by Housing Type

138

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

2 2 Year Built (1) Prior to 1950 74.5 114.9 46.8 24% 1950 to 1969 66.0 96.6 38.1 23% 1970 to 1979 59.4 83.4 33.5 15% 1980 to 1989 51.9 81.4 32.3 14% 1990 to 1999 48.2 94.4 33.7 16% 2000 to 2005 44.7 94.7 34.3 8% Average 58.7 95.0 40.0 Note(s): Source(s): 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008. 2005 Residential Delivered Energy Consumption Intensities, by Vintage Per Square Per Household Per Household

139

Buildings Energy Data Book: 8.2 Residential Sector Water Consumption  

Buildings Energy Data Book (EERE)

1 1 Residential Water Use by Source (Million Gallons per Day) Year 1980 3,400 1985 3,320 1990 3,390 1995 3,390 2000 (3) (3) 3,590 2005 3,830 Note(s): Source(s): 29,430 25,600 1) Public supply water use: water withdrawn by public and private water suppliers that furnish water to at least 25 people or have a minimum of 15 connections. 2) Self-supply water use: Water withdrawn from a groundwater or surface-water source by a user rather than being obtained from a public supply. 3) USGS did not provide estimates of residential use from public supplies in 2000. This value was estimated based on the residential portion of public supply in 1995 and applied to the total public supply water use in 2000. U.S. Geological Survey, Estimated Use of Water in the U.S. in 1985, U.S. Geological Survey Circular 1004, 1988; U.S. Geological Survey, Estimated Use of

140

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

Note: This page contains sample records for the topic "residential sector forecast" 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

Potential Impact of Adopting Maximum Technologies as Minimum Efficiency Performance Standards in the U.S. Residential Sector  

E-Print Network (OSTI)

buildings/appliance_standards/residential/cac_heatp umps_buildings/appliance_standards/residential/fb_tsd_09 07.htmlof Energy Efficiency Standards and Labeling Programs, LBNL

Letschert, Virginie

2010-01-01T23:59:59.000Z

142

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

3 3 Building Type Pre-1995 1995-2005 Pre-1995 1995-2005 Pre-1995 1995-2005 Single-Family 38.4 44.9 102.7 106.2 38.5 35.5 Detached 37.9 44.7 104.5 107.8 38.8 35.4 Attached 43.8 55.5 86.9 85.1 34.2 37.6 Multi-Family 63.8 58.7 58.3 49.2 27.2 24.3 2 to 4 units 69.0 55.1 70.7 59.4 29.5 25.0 5 or more units 61.5 59.6 53.6 47.2 26.3 24.2 Mobile Homes 82.4 57.1 69.6 74.5 29.7 25.2 Note(s): Source(s): 2005 Residential Delivered Energy Consumption Intensities, by Principal Building Type and Vintage Per Square Foot (thousand Btu) (1) Per Household (million Btu) Per Household Member (million Btu) 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average

143

Buildings Energy Data Book: 8.2 Residential Sector Water Consumption  

Buildings Energy Data Book (EERE)

6 6 Residential Water Billing Rate Structures for Community Water Systems Rate Structure Uniform Rates Declining Block Rate Increasing Block Rate Peak Period or Seasonal Rate Separate Flat Fee Annual Connection Fee Combined Flat Fee Other Rate Structures Note(s): Source(s): 3.0% 9.0% 1) Systems serving more than 10,000 users provide service to 82% of the population served by community water systems. Columns do not sum to 100% because some systems use more than one rate structure. 2) Uniform rates charge a set price for each unit of water. Block rates charge a different price for each additional increment of usage. The prices for each increment is higher for increasing block rates and lower for decreasing block rates. Peak rates and seasonal rates charge higher prices when demand is highest. Flat fees charge a set price for

144

Behaviour Oriented Optimisation Strategies for Energy Efficiency in the Residential Sector  

E-Print Network (OSTI)

The aim of this paper is to combine the approaches of engineering and sociology in the assessment of behavioural influences on the energy demand of residential buildings and to define a common language and strategy for their description. For this purpose the calculation methods of the German Energy Conservation Regulations (EnEV 2007) further defined in the DIN 4108-6: 2003-06 will be evaluated to illustrate the relevant linkages to behavioural approaches. So far, there are few attempts to differentiate the large influence of individual behaviour (see Richter 2003, Loga 2003). The assessment of these values and their behavioural implications require a sociological approach towards energy relevant practices. Based on the calculation of the building’s energy balance an analytical framework will be suggested to link the heat demand with the lifestyles of consumers.

Koch, A.; Huber, A.; Avci, N.

2008-10-01T23:59:59.000Z

145

A Statistical Model to Assess Indirect CO2 Emissions of the UAE Residential Sector  

E-Print Network (OSTI)

This study presents a regional bottom-up model for assessing space cooling energy and related greenhouse gas emissions. The model was developed with the aim of improving the quality and quantity of cooling energy and emission data, especially for the benefit of local decision making. Based on a benchmarking study, a representative archetype was developed, simulation software used and linear statistical model constructed. This model explores the way in which CO2 emission levels are affected by different energy efficiency measures to reduce cooling energy consumption in buildings. The analysis showed that improving building energy efficiency could generate considerable carbon emissions reduction credits with competitive benefit. The developed model was found to be capable in selecting cost-effective, environmentally-preferred building efficiency measures and evaluating the future trend of CO2 emissions in the residential building of Al-Ain city.

Radhi, H.; Fikry, F.

2010-01-01T23:59:59.000Z

146

Is Efficiency Enough? Towards a New Framework for Carbon Savingsin the California Residential Sector  

SciTech Connect

The overall implementation of energy efficiency in the United States is not adequately aligned with the environmental benefits claimed for efficiency, because it does not consider absolute levels of energy use, pollutant emissions, or consumption. In some ways, promoting energy efficiency may even encourage consumption. A more effective basis for environmental policy could be achieved by recognizing the degree and nature of the synchronization between environmental objectives and efficiency. This research seeks to motivate and initiate exploration of alternative ways of defining efficiency or otherwise moderating energy use toward reaching environmental objectives, as applicable to residential electricity use in California. The report offers three main recommendations: (1) produce definitions of efficiency that better integrate absolute consumption, (2) attend to the deeper social messages of energy efficiency communications, and (3) develop a more critical perspective on benefits and limitations of energy efficiency for delivering environmental benefits. In keeping with the exploratory nature of this project, the report also identifies ten questions for further investigation.

Moezzi, Mithra; Diamond, Rick

2005-10-01T23:59:59.000Z

147

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

20 20 Site Consumption Primary Consumption Total Residential Industry Electric Gen. Transportation Residential Industry Transportation (quads) 1980 5% 28% 8% 56% | 8% 31% 56% 34.2 1981 5% 26% 7% 59% | 7% 29% 59% 31.9 1982 5% 26% 5% 61% | 6% 28% 61% 30.2 1983 4% 25% 5% 62% | 6% 27% 62% 30.1 1984 5% 26% 4% 61% | 6% 27% 61% 31.1 1985 5% 25% 4% 63% | 6% 26% 63% 30.9 1986 5% 24% 5% 63% | 6% 26% 63% 32.2 1987 5% 25% 4% 63% | 6% 26% 63% 32.9 1988 5% 24% 5% 63% | 6% 26% 63% 34.2 1989 5% 24% 5% 63% | 7% 25% 63% 34.2 1990 4% 25% 4% 64% | 5% 26% 64% 33.6 1991 4% 24% 4% 65% | 5% 26% 65% 32.8 1992 4% 26% 3% 65% | 5% 27% 65% 33.5 1993 4% 25% 3% 65% | 5% 26% 65% 33.8 1994 4% 25% 3% 65% | 5% 26% 65% 34.7 1995 4% 25% 2% 67% | 5% 26% 67% 34.6 1996 4% 25% 2% 66% | 5% 26% 66% 35.8 1997 4% 26% 3% 66% | 5% 26% 66% 36.3 1998 3% 25% 4% 66% | 5% 26% 66% 36.9 1999 4% 25% 3% 66% | 5% 26% 66% 38.0 2000 4% 24% 3% 67% | 5% 25% 67% 38.4 2001 4% 24% 3% 67% | 5% 25% 67% 38.3 2002 4% 24% 3% 68% | 5% 25% 68% 38.4 2003

148

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

9 9 Total Residential Industry Electric Gen. Transportation Residential Industry Transportation (quads) 1980 24% 41% 19% 3% | 30% 49% 3% 20.22 1981 23% 42% 19% 3% | 30% 49% 3% 19.74 1982 26% 39% 18% 3% | 32% 45% 3% 18.36 1983 26% 39% 17% 3% | 32% 46% 3% 17.20 1984 25% 40% 17% 3% | 31% 47% 3% 18.38 1985 25% 40% 18% 3% | 32% 46% 3% 17.70 1986 26% 40% 16% 3% | 32% 46% 3% 16.59 1987 25% 41% 17% 3% | 31% 47% 3% 17.63 1988 26% 42% 15% 3% | 31% 47% 3% 18.44 1989 25% 41% 16% 3% | 30% 47% 3% 19.56 1990 23% 43% 17% 3% | 29% 49% 4% 19.57 1991 23% 43% 17% 3% | 29% 49% 3% 20.03 1992 23% 43% 17% 3% | 29% 49% 3% 20.71 1993 24% 43% 17% 3% | 30% 48% 3% 21.24 1994 23% 42% 18% 3% | 29% 48% 3% 21.75 1995 22% 42% 19% 3% | 28% 49% 3% 22.71 1996 23% 43% 17% 3% | 29% 49% 3% 23.14 1997 22% 43% 18% 3% | 28% 49% 3% 23.34 1998 20% 43% 20% 3% | 27% 50% 3% 22.86 1999 21% 41% 21% 3% | 28% 48% 3% 22.88 2000 21% 40% 22% 3% | 29% 47% 3% 23.66 2001 21% 38% 24% 3% | 30% 45% 3% 22.69 2002 21% 38% 24% 3% | 30% 45%

149

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

0 0 Region (1) Northeast 73.5 122.2 47.7 24% New England 77.0 129.4 55.3 7% Middle Atlantic 72.2 119.7 45.3 17% Midwest 58.9 113.5 46.0 28% East North Central 61.1 117.7 47.3 20% West North Central 54.0 104.1 42.9 8% South 51.5 79.8 31.6 31% South Atlantic 47.4 76.1 30.4 16% East South Central 56.6 87.3 36.1 6% West South Central 56.6 82.4 31.4 9% West 56.6 77.4 28.1 18% Mountain 54.4 89.8 33.7 6% Pacific 58.0 71.8 25.7 11% U.S. Average 58.7 94.9 37.0 100% Note(s): Source(s): 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet.

150

Buildings Energy Data Book: 8.2 Residential Sector Water Consumption  

Buildings Energy Data Book (EERE)

2 2 1999 Single-Family Home Daily Water Consumption by End Use (Gallons per Capita) (1) Fixture/End Use Toilet 18.5 18.3% Clothes Washer 15 14.9% Shower 11.6 11.5% Faucet 10.9 10.8% Other Domestic 1.6 1.6% Bath 1.2 1.2% Dishwasher 1 1.0% Leaks 9.5 9.4% Outdoor Use (2) 31.7 31.4% Total (2) 101 100% Note(s): Source(s): Average gallons Total Use per capita per day Percent 1) Based analysis of 1,188 single-family homes at 12 study locations. 2) Total Water use derived from USGS. Outdoor use is the difference between total and indoor uses. American Water Works Association Research Foundation, Residential End Uses of Water, 1999; U.S. Geological Survey, Estimated Use of Water in the U.S. in 2000, U.S. Geological Survey Circular 1268, 2004, Table 6, p. 17; and Vickers, Amy, Handbook of Water Use and Conservation, June 2002, p. 15.

151

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

152

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

5 5 Natural Fuel Other Renw. Site Site Primary Gas Oil LPG Fuel(1) En.(2) Electric Total Percent Electric (3) Total Percent Space Heating (4) 3.50 0.53 0.30 0.04 0.43 0.44 5.23 44.7% | 1.35 6.15 27.8% Water Heating 1.29 0.10 0.07 0.01 0.45 1.92 16.4% | 1.38 2.86 12.9% Space Cooling 0.00 1.08 1.08 9.2% | 3.34 3.34 15.1% Lighting 0.69 0.69 5.9% | 2.13 2.13 9.7% Refrigeration (6) 0.45 0.45 3.9% | 1.41 1.41 6.4% Electronics (5) 0.54 0.54 4.7% | 1.68 1.68 7.6% Wet Cleaning (7) 0.06 0.33 0.38 3.3% | 1.01 1.06 4.8% Cooking 0.22 0.03 0.18 0.43 3.7% | 0.57 0.81 3.7% Computers 0.17 0.17 1.5% | 0.53 0.53 2.4% Other (8) 0.00 0.16 0.01 0.20 0.37 3.2% | 0.63 0.80 3.6% Adjust to SEDS (9) 0.42 0.42 3.6% | 1.29 1.29 5.8% Total 5.06 0.63 0.56 0.04 0.45 4.95 11.69 100% | 15.34 22.07 100% Note(s): Source(s): 2010 Residential Energy End-Use Splits, by Fuel Type (Quadrillion Btu) Primary 1) Kerosene and coal are assumed attributable to space heating. 2) Comprised of wood space heating (0.42 quad), solar water heating (0.01

153

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

8 8 Natural Fuel Other Renw. Site Site Primary Gas Oil LPG Fuel(1) En.(2) Electric Total Percent Electric (3) Total Percent Space Heating (4) 3.20 0.31 0.22 0.03 0.46 0.49 4.72 38.9% | 1.45 5.67 23.9% Water Heating 1.27 0.04 0.03 0.02 0.54 1.90 15.6% | 1.60 2.96 12.5% Space Cooling 0.00 1.25 1.25 10.3% | 3.68 3.68 15.5% Lighting 0.48 0.48 3.9% | 1.41 1.41 5.9% Refrigeration (5) 0.52 0.52 4.3% | 1.54 1.54 6.5% Electronics (6) 0.44 0.44 3.6% | 1.29 1.29 5.4% Wet Cleaning (7) 0.07 0.32 0.39 3.2% | 0.95 1.01 4.3% Cooking 0.23 0.02 0.15 0.40 3.3% | 0.44 0.69 2.9% Computers 0.27 0.27 2.2% | 0.79 0.79 3.3% Other (8) 0.00 0.22 0.07 1.48 1.77 14.6% | 4.35 4.64 19.6% Total 4.76 0.35 0.51 0.03 0.55 5.94 12.14 100% | 17.50 23.69 100% Note(s): Source(s): 2035 Residential Energy End-Use Splits, by Fuel Type (Quadrillion Btu) Primary 1) Kerosene and coal are assumed attributable to space heating. 2) Comprised of wood space heating (0.44 quad), solar water heating (0.02

154

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

7 7 Natural Fuel Other Renw. Site Site Primary Gas Oil LPG Fuel(1) En.(2) Electric Total Percent Electric (3) Total Percent Space Heating (4) 3.28 0.38 0.24 0.03 0.46 0.46 4.85 41.5% | 1.40 5.78 25.8% Water Heating 1.32 0.05 0.04 0.02 0.53 1.96 16.8% | 1.60 3.03 13.5% Space Cooling 0.00 1.12 1.12 9.6% | 3.38 3.38 15.1% Lighting 0.47 0.47 4.0% | 1.42 1.42 6.3% Refrigeration (5) 0.48 0.48 4.1% | 1.45 1.45 6.5% Electronics (6) 0.37 0.37 3.2% | 1.12 1.12 5.0% Wet Cleaning (7) 0.06 0.30 0.37 3.1% | 0.91 0.98 4.4% Cooking 0.22 0.03 0.13 0.38 3.2% | 0.40 0.64 2.9% Computers 0.24 0.24 2.0% | 0.72 0.72 3.2% Other (8) 0.00 0.20 0.07 1.20 1.46 12.5% | 3.61 3.87 17.3% Total 4.88 0.43 0.50 0.03 1.00 5.30 11.69 100% | 16.00 22.39 100% Note(s): Source(s): 2025 Residential Energy End-Use Splits, by Fuel Type (Quadrillion Btu) Primary 1) Kerosene and coal are assumed attributable to space heating. 2) Comprised of wood space heating (0.43 quad), solar water heating (0.02

155

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book (EERE)

6 6 Natural Fuel Other Renw. Site Site Primary Gas Oil LPG Fuel(1) En.(2) Electric Total Percent Electric (3) Total Percent Space Heating (4) 3.40 0.48 0.26 0.03 0.44 0.42 5.03 44.2% | 1.27 5.88 27.9% Water Heating 1.31 0.07 0.05 0.02 0.48 1.92 16.9% | 1.44 2.88 13.7% Space Cooling 0.00 1.02 1.02 8.9% | 3.07 3.07 14.6% Lighting 0.53 0.53 4.6% | 1.60 1.60 7.6% Refrigeration (5) 0.45 0.45 4.0% | 1.37 1.37 6.5% Electronics (6) 0.33 0.33 2.9% | 0.99 0.99 4.7% Wet Cleaning (7) 0.06 0.33 0.39 3.4% | 0.98 1.04 5.0% Cooking 0.22 0.03 0.11 0.36 3.1% | 0.34 0.59 2.8% Computers 0.19 0.19 1.7% | 0.57 0.57 2.7% Other (8) 0.00 0.17 0.05 0.94 1.17 10.2% | 2.85 3.07 14.6% Total 4.99 0.55 0.51 0.03 0.51 4.79 11.38 100% | 14.47 21.06 100% Note(s): Source(s): 2015 Residential Energy End-Use Splits, by Fuel Type (Quadrillion Btu) Primary 1) Kerosene and coal are assumed attributable to space heating. 2) Comprised of wood space heating (0.43 quad), solar water heating (0.02

156

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.

157

Burlington Electric Department - Residential Energy Efficiency...  

Open Energy Info (EERE)

Sector Residential Eligible Technologies Clothes Washers, Lighting, Water Heaters, LED Lighting, Tankless Water Heaters Active Incentive Yes Implementing Sector Utility...

158

South Alabama Electric Cooperative - Residential Energy Efficiency...  

Open Energy Info (EERE)

Sector Residential Eligible Technologies Building Insulation, Doors, Heat pumps, Windows, Geothermal Heat Pumps Active Incentive Yes Implementing Sector Utility Energy...

159

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

160

A Water Conservation Scenario for the Residential and Industrial Sectors in California: Potential Saveings of Water and Related Energy  

E-Print Network (OSTI)

A WATER CONSERVATION SCENARIO FOR THE RESIDENTIAL ANDWater 'consumption, water conservation. City of Sacramento.Daniel Stockton. Water conservation. Contra Costa County

Benenson, P.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

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

162

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

163

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

164

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.

165

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

E-Print Network (OSTI)

LPG Furnace Oil Furnace Electric Heat Pump Gas BoilerOil Boiler Electric Room Heater Gas Room Heater Wood Stove (Electric Heat Pump Gas Boiler Oil Boiler Electric Room Gas

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

166

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

E-Print Network (OSTI)

Homes End-Use Equipment Type Equipment Market Shares Index Heating ElecFurnace Gas Furnace LPG Furnace OilHomes (millions) End-Use Equipment Type Appliance stock in millions of units Index Heating FJec Furnace Gas Furnace L P G Furnace OilHomes End-Use Equipment Type Units Efficiency for Stock Equipment Index Heating Elec Furnace Btu.out/Wh.in Gas Furnace AFUE LPG Furnace AFUE Oil

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

167

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

E-Print Network (OSTI)

A comparison of national energy consumption by fuel typeenergy consumption in homes under differing assumptions, scenarios, and policies. At the national

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

168

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

E-Print Network (OSTI)

Natural Gas Oil Lighting 0-1 hrs 1-2 his 2-3 hrs Usage levelgas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs Usagegas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs Usage

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

169

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

E-Print Network (OSTI)

year consumption estimates. DISCUSSION The Importance of Miscellaneous ElectricityConsumption of New Equipment Index kWh/Year MMBtu/Year MMBtu/Year ElectricityConsumption of Equipment in Existing Homes Index kWh/Year MMBtu/Year MMBtu/Year Electricity

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

170

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

E-Print Network (OSTI)

Richard E. Brown, James W. Hanford, Alan H . Sanstad, andFrancis X . , James W. Hanford, Richard E. Brown, Alan H.place for these end-uses (Hanford et al. 1994, Hwang et al.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

171

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

E-Print Network (OSTI)

=1 Index 1990=1 Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrsMiscellaneous Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrsMiscellaneous Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrs

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

172

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

E-Print Network (OSTI)

Description Prices for oil, gas, electricity, liquidElectric Electric Electric Gas Oil Electric ElectricElectric Gas Electric Gas Oil Electric Electric Gas Oil

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

173

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

E-Print Network (OSTI)

gas Oil Secondary Heating Wood Stove Secondary Cooling RoomTotal Secondary Heating Wood Stove Secondary Cooling Room ACConsumption Secondary Heating Wood Stove Secondary Cooling

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

174

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

E-Print Network (OSTI)

heaters, clothes washers, dishwashers, lighting, cooking,Refrigerators Water Heaters Dishwashers Clothes Washersof its useful life. For dishwashers, clothes washers, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

175

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

E-Print Network (OSTI)

light bulbs having designated usage level in the average house. (3) Refrigerator marketlight bulbs having designated usage level in the average house. (3) Refrigerator market

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

176

Energy efficiency standards for residential and commercial equipment: Additional opportunities  

E-Print Network (OSTI)

Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors. ”Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors. ”Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors. ”

Rosenquist, Greg; McNeil, Michael; Iyer, Maithili; Meyers, Steve; McMahon, Jim

2004-01-01T23:59:59.000Z

177

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

178

Operational energy consumption and GHG emissions in residential sector in urban China : an empirical study in Jinan  

E-Print Network (OSTI)

Driven by rapid urbanization and increasing household incomes, residential energy consumption in urban China has been growing steadily in the past decade, posing critical energy and greenhouse gas emission challenges. ...

Zhang, Jiyang, M.C.P. Massachusetts Institute of Technology

2010-01-01T23:59:59.000Z

179

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

180

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

Note: This page contains sample records for the topic "residential sector forecast" 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

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

182

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

183

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

184

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...

185

Sector-specific issues and reporting methodologies supporting the General Guidelines for the voluntary reporting of greenhouse gases under Section 1605(b) of the Energy Policy Act of 1992. Volume 1: Part 1, Electricity supply sector; Part 2, Residential and commercial buildings sector; Part 3, Industrial sector  

Science Conference Proceedings (OSTI)

DOE encourages you to report your achievements in reducing greenhouse gas emissions and sequestering carbon under this program. Global climate change is increasingly being recognized as a threat that individuals and organizations can take action against. If you are among those taking action, reporting your projects may lead to recognition for you, motivation for others, and synergistic learning for the global community. This report discusses the reporting process for the voluntary detailed guidance in the sectoral supporting documents for electricity supply, residential and commercial buildings, industry, transportation, forestry, and agriculture. You may have reportable projects in several sectors; you may report them separately or capture and report the total effects on an entity-wide report.

Not Available

1994-10-01T23:59:59.000Z

186

Application analysis of solar total energy systems to the residential sector. Volume IV, market penetration. Final report  

DOE Green Energy (OSTI)

This volume first describes the residential consumption of energy in each of the 11 STES regions by fuel type and end-use category. The current and projected costs and availability of fossil fuels and electricity for the STES regions are reported. Projections are made concerning residential building construction and the potential market for residential STES. The effects of STES ownership options, institutional constraints, and possible government actions on market penetration potential were considered. Capital costs for two types of STES were determined, those based on organic Rankine cycle (ORC) heat engines and those based on flat plate, water-cooled photovoltaic arrays. Both types of systems utilized parabolic trough collectors. The capital cost differential between conventional and STE systems was calculated on an incremental cost per dwelling unit for comparison with projected fuel savings in the market penetration analysis. The market penetration analysis was planned in two phases, a preliminary analysis of each of the geographical regions for each of the STE systems considered; and a final, more precise analysis of those regions and systems showing promise of significant market penetration. However, the preliminary analysis revealed no geographical regions in which any of the STES considered promised to be competitive with conventional energy systems using utility services at the prices projected for future energy supplies in the residential market. Because no promising situations were found, the analysis was directed toward an examination of the parameters involved in an effort to identify those factors which make a residential STES less attractive than similar systems in the commercial and industrial areas. Results are reported. (WHK)

Not Available

1979-07-01T23:59:59.000Z

187

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

188

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

189

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

190

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.

191

A Rank Approach to Equity Forecast Construction  

E-Print Network (OSTI)

that are fit for their purpose; for example, returningaggregate county and sector forecasts that are consistent by construction....

Satchell, Stephen E; Wright, Stephen M

2006-03-14T23:59:59.000Z

192

North Arkansas Electric Cooperative, Inc - Residential Energy...  

Open Energy Info (EERE)

Sector Residential Eligible Technologies Doors, DuctAir sealing, Heat pumps, Windows Active Incentive Yes Implementing Sector Utility Energy Category Energy Efficiency...

193

Clark Public Utilities - Residential Weatherization Loan Program...  

Open Energy Info (EERE)

Sector Residential Eligible Technologies Building Insulation, DuctAir sealing, Windows Active Incentive Yes Implementing Sector Utility Energy Category Energy Efficiency...

194

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

195

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

196

Application analysis of solar total energy systems to the residential sector. Volume III, conceptual design. Final report  

DOE Green Energy (OSTI)

The objective of the work described in this volume was to conceptualize suitable designs for solar total energy systems for the following residential market segments: single-family detached homes, single-family attached units (townhouses), low-rise apartments, and high-rise apartments. Conceptual designs for the total energy systems are based on parabolic trough collectors in conjunction with a 100 kWe organic Rankine cycle heat engine or a flat-plate, water-cooled photovoltaic array. The ORC-based systems are designed to operate as either independent (stand alone) systems that burn fossil fuel for backup electricity or as systems that purchase electricity from a utility grid for electrical backup. The ORC designs are classified as (1) a high temperature system designed to operate at 600/sup 0/F and (2) a low temperature system designed to operate at 300/sup 0/F. The 600/sup 0/F ORC system that purchases grid electricity as backup utilizes the thermal tracking principle and the 300/sup 0/F ORC system tracks the combined thermal and electrical loads. Reject heat from the condenser supplies thermal energy for heating and cooling. All of the ORC systems utilize fossil fuel boilers to supply backup thermal energy to both the primary (electrical generating) cycle and the secondary (thermal) cycle. Space heating is supplied by a central hot water (hydronic) system and a central absorption chiller supplies the space cooling loads. A central hot water system supplies domestic hot water. The photovoltaic system uses a central electrical vapor compression air conditioning system for space cooling, with space heating and domestic hot water provided by reject heat from the water-cooled array. All of the systems incorporate low temperature thermal storage (based on water as the storage medium) and lead--acid battery storage for electricity; in addition, the 600/sup 0/F ORC system uses a therminol-rock high temperature storage for the primary cycle. (WHK)

Not Available

1979-07-01T23:59:59.000Z

197

Toward a National Plan for the Accelerated Commercialization of Solar Energy: residential/commercial buildings market sector workbook  

Science Conference Proceedings (OSTI)

This workbook contains preliminary data and assumptions used during the preparation of inputs to a National Plan for the Accelerated Commercialization of Solar Energy (NPAC). The workbook indicates the market potential, competitive position, market penetration, and technological characteristics of solar technologies for this market sector over the next twenty years. The workbook also presents projections of the mix of solar technologies by US Census Regions. In some cases, data have been aggregated to the national level. Emphasis of the workbook is on a mid-price fuel scenario, Option II, that meets about a 20% solar goal by the year 2000. The energy demand for the mid-price scenario is projected at 115 quads in the year 2000. The workbook, prepared in April 1979, represents government policies and programs anticipated at that time.

Taul, Jr., J. W.; de Jong, D. L.

1980-01-01T23:59:59.000Z

198

Black Hills Power - Residential Customer Rebate Program (South...  

Open Energy Info (EERE)

Program Applicable Sector Multi-Family Residential, Residential Eligible Technologies Energy Mgmt. SystemsBuilding Controls, Heat pumps, Water Heaters, Geothermal Heat Pumps,...

199

Black Hills Power - Residential Customer Rebate Program (Wyoming...  

Open Energy Info (EERE)

Program Applicable Sector Multi-Family Residential, Residential Eligible Technologies Energy Mgmt. SystemsBuilding Controls, Heat pumps, Water Heaters, Geothermal Heat Pumps,...

200

U.S. Propane Demand Sectors (1996)  

U.S. Energy Information Administration (EIA)

The residential and commercial sector and the chemical sector are the largest end users of propane in the U.S., accounting for 34% and 41% ...

Note: This page contains sample records for the topic "residential sector forecast" 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

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

202

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

203

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

204

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

205

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

206

Residential and Commercial Buildings Sector  

U.S. Energy Information Administration (EIA)

Also assume that the fan, both before and after project implementa-tion, was rated at 3 thousand cubic feet per minute (MCFM). The estimation was completed as follows:

207

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

208

Residential Energy Efficiency Workshop - U.S. Energy Information ...  

U.S. Energy Information Administration (EIA)

Notes on the Energy Information Administration's summary session on Residential Sector Energy-Efficiency Workshop on February 13, 1996

209

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

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

210

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

211

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)

212

Chelan County PUD - Residential Weatherization Rebate Program...  

Open Energy Info (EERE)

Residential Eligible Technologies Building Insulation, Doors, DuctAir sealing, Windows Active Incentive Yes Implementing Sector Utility Energy Category Energy Efficiency...

213

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...

214

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...

215

Beräkning av koldioxidutsläppet frćn bostadssektorn i Stockholms län; Estimation of Carbon Dioxide Emissions from the Residential Building Sector in the county of Stockholm.  

E-Print Network (OSTI)

?? During the last decades the housing sector has increased continuously, and housings and services accounted for 40 % of the energy usage in Sweden… (more)

Chen, Guojing

2013-01-01T23:59:59.000Z

216

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

217

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

218

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

219

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

220

Residential Building Stockg Assessment (RBSA)for  

E-Print Network (OSTI)

9/4/2013 1 Residential Building Stockg Assessment (RBSA)for Multi-Family Housing Tom Eckman Objectives Characterize Residential Sector Building Stock ­ Single Family (Four-plex and below) l if il ( i Pacific Northwest Residential Energy Survey (PNWRES92)Survey (PNWRES92) NEEA Survey of Baseline

Note: This page contains sample records for the topic "residential sector forecast" 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

Geothermal Energy Market Study on the Atlantic Coastal Plain. Geothermal Energy Market penetration: development of a model for the residential sector  

SciTech Connect

A model has been developed that examines the feasibility of using geothermal technology in heating residential structures. Specific account is taken of the small contribution of new housing to the total stock in any given year and of the durability of houses and their furnaces. Both aspects constrain the penetration of geothermal energy into the residential market. After a discussion of other market penetration paradigms, a simple model of market penetration is developed that is based on the premise that homeowners will not abandon an existing furnace until its economic life is over. Next, behavioral parameters are discussed and the model is extended from 20 to 40 years. Finally, methods are discussed for collecting the needed data to determine market penetration, and ideas are proposed of ways to induce homeowners to give up economically viable furnaces to allow the firm providing the energy to reduce costs.

Goodman, A.C.

1979-09-01T23:59:59.000Z

222

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

223

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

224

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

225

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

226

Customization and Marketing of Monsoon Forecasts A CSIRCMMACS Synergy  

E-Print Network (OSTI)

Customization and Marketing of Monsoon Forecasts A CSIRCMMACS Synergy Criteria for Technical forecasts of monsoon can significantly aid many sectors like agriculture, power and production industries to the operational forecast, to develop and deliver customized monsoon forecasts based on user need is required

Swathi, P S

227

R/ECON July 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON July 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF JULY 2001 NEW JERSEY each year. The R/ECONTM forecast for New Jersey looks for growth in real output of 2.6 percent years. Over the forecast period, both the construction and manufacturing sectors will lose jobs

228

R/ECON April 2000 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON April 2000 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF APRIL 2000 NEW JERSEY to more inflation and higher interest rates. The R/ECON TM forecast for New Jersey looks for employment.6% a year over the forecast period. The services and trade sectors will provide 90% of the net increase

229

Energy Perspectives: Industrial and transportation sectors ...  

U.S. Energy Information Administration (EIA)

Since 2008, energy use in the transportation, residential, and commercial sectors stayed relatively constant or fell slightly. Industrial consumption grew in 2010 and ...

230

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

231

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

232

Japan's Residential Energy Demand Outlook to 2030 Considering Energy Efficiency Standards "Top-Runner Approach"  

E-Print Network (OSTI)

L ABORATORY Japan’s Residential Energy Demand Outlook tol i f o r n i a Japan’s Residential Energy Demand Outlook toParticularly in Japan’s residential sector, where energy

Komiyama, Ryoichi

2008-01-01T23:59:59.000Z

233

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

234

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

235

Electricity Markets and Policy Group Energy Analysis Department Financing Non-Residential  

E-Print Network (OSTI)

Electricity Markets and Policy Group · Energy Analysis Department 1 Financing Non-Residential Introduction · Growth in the non-residential PV sector has outpaced that of the residential PV sector in recent years: by one estimate, US non-residential PV capacity has grown from less than half of aggregate annual

236

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

237

BUILDINGS SECTOR DEMAND-SIDE EFFICIENCY TECHNOLOGY SUMMARIES  

E-Print Network (OSTI)

: Small Commercial, Residential Author: Haider Taha VII. Solar Domestic Water Heaters........................................................................... 59 End-Use: Water Heating Sector: Residential Author: Jim Lutz VIII. Heat Pump Water Heaters ................................................................................. 63 End-Use: Water Heating Sector: Residential Author: Jim Lutz IX. Energy-Efficient Motors

238

DSM Electricity Savings Potential in the Buildings Sector in APP Countries  

E-Print Network (OSTI)

Developments in Electricity Demand Management – Lessons24 Table 4. Electricity Demand Projections, Energy and3. APP Base Case Electricity Demand Forecast –Residential

McNeil, MIchael

2011-01-01T23:59:59.000Z

239

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

240

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2012-11-15T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2013-10-10T23:59:59.000Z

242

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

243

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...

244

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...

245

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,

246

U.S. Residential Electricity Use Trends  

Science Conference Proceedings (OSTI)

This report summarizes electricity end use in the residential sector, with national level and regional level data. Load shapes for residential energy use are also provided, with focus on two example U.S. Census region divisions: East South Central and Mountain.

2008-08-29T23:59:59.000Z

247

RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF OCTOBER 1998  

E-Print Network (OSTI)

RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF OCTOBER 1998 NEW JERSEY: EXPANSION CONTINUES/ECONTM forecast for New Jersey is for a continuation of the current expansion but at a much reduced rate. In 1998 and throughout the forecast period will be in various service sectors and in retail trade. Health services

248

The Path to Low Energy Residential Design  

Science Conference Proceedings (OSTI)

The Department of EnergyBuilding Technologies Program is divided between the commercial and residential sectors. Under the residential sector, two main areas exist - Home Performance with Energy Star, and the Building America program. The Building America program focuses on improving the efficiency of the approximately 1.5 million new homes built each year. These improvements are accomplished through research, development, demonstrations, and technology transfer of system-based strategies. These efforts...

2009-12-16T23:59:59.000Z

249

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

250

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":""}]}

251

State Residential Energy Consumption Shares  

Gasoline and Diesel Fuel Update (EIA)

This next slide shows what fuels are used in the residential market. When a This next slide shows what fuels are used in the residential market. When a energy supply event happens, particularly severe winter weather, it is this sector that the government becomes most concerned about. As you can see, natural gas is very important to the residential sector not only in DC, MD and VA but in the United States as well. DC residents use more natural gas for home heating than do MD and VA. While residents use heating oil in all three states, this fuel plays an important role in MD and VA. Note: kerosene is included in the distillate category because it is an important fuel to rural households in MD and VA. MD and VA rely more on electricity than DC. Both MD and VA use propane as well. While there are some similarities in this chart, it is interesting to note

252

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)

253

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

254

Progress towards Managing Residential Electricity Demand: Impacts of Standards and Labeling for Refrigerators and Air Conditioners in India  

SciTech Connect

The development of Energy Efficiency Standards and Labeling (EES&L) began in earnest in India in 2001 with the Energy Conservation Act and the establishment of the Indian Bureau of Energy Efficiency (BEE). The first main residential appliance to be targeted was refrigerators, soon to be followed by room air conditioners. Both of these appliances are of critical importance to India's residential electricity demand. About 15percent of Indian households own a refrigerator, and sales total about 4 million per year, but are growing. At the same time, the Indian refrigerator market has seen a strong trend towards larger and more consumptive frost-free units. Room air conditioners in India have traditionally been sold to commercial sector customers, but an increasing number are going to the residential sector. Room air conditioner sales growth in India peaked in the last few years at 20percent per year. In this paper, we perform an engineering-based analysis using data specific to Indian appliances. We evaluate costs and benefits to residential and commercial sector consumers from increased equipment costs and utility bill savings. The analysis finds that, while the BEE scheme presents net benefits to consumers, there remain opportunities for efficiency improvement that would optimize consumer benefits, according to Life Cycle Cost analysis. Due to the large and growing market for refrigerators and air conditioners in India, we forecast large impacts from the standards and labeling program as scheduled. By 2030, this program, if fully implemented would reduce Indian residential electricity consumption by 55 TWh. Overall savings through 2030 totals 385 TWh. Finally, while efficiency levels have been set for several years for refrigerators, labels and MEPS for these products remain voluntary. We therefore consider the negative impact of this delay of implementation to energy and financial savings achievable by 2030.

McNeil, Michael A.; Iyer, Maithili

2009-05-30T23:59:59.000Z

255

Progress towards Managing Residential Electricity Demand: Impacts of Standards and Labeling for Refrigerators and Air Conditioners in India  

SciTech Connect

The development of Energy Efficiency Standards and Labeling (EES&L) began in earnest in India in 2001 with the Energy Conservation Act and the establishment of the Indian Bureau of Energy Efficiency (BEE). The first main residential appliance to be targeted was refrigerators, soon to be followed by room air conditioners. Both of these appliances are of critical importance to India's residential electricity demand. About 15percent of Indian households own a refrigerator, and sales total about 4 million per year, but are growing. At the same time, the Indian refrigerator market has seen a strong trend towards larger and more consumptive frost-free units. Room air conditioners in India have traditionally been sold to commercial sector customers, but an increasing number are going to the residential sector. Room air conditioner sales growth in India peaked in the last few years at 20percent per year. In this paper, we perform an engineering-based analysis using data specific to Indian appliances. We evaluate costs and benefits to residential and commercial sector consumers from increased equipment costs and utility bill savings. The analysis finds that, while the BEE scheme presents net benefits to consumers, there remain opportunities for efficiency improvement that would optimize consumer benefits, according to Life Cycle Cost analysis. Due to the large and growing market for refrigerators and air conditioners in India, we forecast large impacts from the standards and labeling program as scheduled. By 2030, this program, if fully implemented would reduce Indian residential electricity consumption by 55 TWh. Overall savings through 2030 totals 385 TWh. Finally, while efficiency levels have been set for several years for refrigerators, labels and MEPS for these products remain voluntary. We therefore consider the negative impact of this delay of implementation to energy and financial savings achievable by 2030.

McNeil, Michael A.; Iyer, Maithili

2009-05-30T23:59:59.000Z

256

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

257

> 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

258

Products and Service of Center for Weather Forecast and Climate Studies  

E-Print Network (OSTI)

LOG O Products and Service of Center for Weather Forecast and Climate Studies Simone Sievert da (INPE) #12;Talk Outline CPTEC/INPE Operational Forecast Systems (time scales) Modeling Forecast-Brazil Space Sector Workshop, São José dos Campos 26 - 27 August. 2010 CPTEC: Forecasting Modeling Scales Time

259

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

260

Electricity Use in the Pacific Northwest: Utility Historical Sales by Sector, 1989 and Preceding Years.  

SciTech Connect

This report officially releases the compilation of regional 1989 retail customer sector sales data by the Bonneville Power Administration. This report is intended to enable detailed examination of annual regional electricity consumption. It gives statistics covering the time period 1970--1989, and also provides observations based on statistics covering the 1983--1989 time period. The electricity use report is the only information source that provides data obtained from each utility in the region based on the amount of electricity they sell to consumers annually. Data is provided on each retail customer sector: residential, commercial, industrial, direct-service industrial, and irrigation. The data specifically supports forecasting activities, rate development, conservation and market assessments, and conservation and market program development and delivery. All of these activities require a detailed look at electricity use. 25 figs., 34 tabs.

United States. Bonneville Power Administration.

1990-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

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

262

Residential Energy Efficiency Stakeholder Meeting - Spring 2012 |  

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

Residential Energy Efficiency Stakeholder Meeting - Spring 2012 Residential Energy Efficiency Stakeholder Meeting - Spring 2012 Residential Energy Efficiency Stakeholder Meeting - Spring 2012 The U.S. Department of Energy (DOE) Building America program held the second annual Residential Energy Efficiency Stakeholder Meeting on February 29-March 2, 2012, in Austin, Texas. At this meeting, hundreds of building industry professionals came together to share their perspective on the most current innovation projects in the residential buildings sector. This meeting provided an opportunity for researchers and industry stakeholders to showcase and discuss the latest in cutting-edge, energy-efficient residential building technologies and practices. The meeting also included working sessions from each Standing Technical Committee (STC), which outlined work that will best assist in overcoming

263

sector | OpenEI  

Open Energy Info (EERE)

sector sector Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 5, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption sector South Atlantic Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - South Atlantic- Reference Case (xls, 297.6 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

264

OpenEI - residential sector key indicators  

Open Energy Info (EERE)

class"field-items">

http:www.eia.doe.govforecastsaeo
...

265

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

266

Environmental assessment in support of proposed voluntary energy conservation standard for new residential buildings  

Science Conference Proceedings (OSTI)

The objective of this environmental assessment (EA) is to identify the potential environmental impacts that could result from the proposed voluntary residential standard (VOLRES) on private sector construction of new residential buildings. 49 refs., 15 tabs.

Hadley, D.L.; Parker, G.B.; Callaway, J.W.; Marsh, S.J.; Roop, J.M.; Taylor, Z.T.

1989-06-01T23:59:59.000Z

267

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

268

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

269

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

270

Improving the Energy Efficiency of Residential Buildings | Department of  

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

Residential Buildings Residential Buildings Improving the Energy Efficiency of Residential Buildings Visitors Tour Solar Decathlon Homes Featuring the Latest in Energy Efficient Building Technology. Learn More Visitors Tour Solar Decathlon Homes Featuring the Latest in Energy Efficient Building Technology. Learn More The Building Technologies Office (BTO) collaborates with the residential building industry to improve the energy efficiency of both new and existing homes. By developing, demonstrating, and deploying cost-effective solutions, BTO strives to reduce energy consumption across the residential building sector by at least 50%. Research and Development Conduct research that focuses on engineering solutions to design, test, and

271

Financial modeling of consumer discount rate in residential solar photovoltaic purchasing decisions.  

E-Print Network (OSTI)

??Diffusion of microgeneration technologies, particularly rooftop photovoltaic (PV), represents a key option in reducing emissions in the residential sector. This thesis uses a uniquely rich… (more)

Sigrin, Benjamin O.

2013-01-01T23:59:59.000Z

272

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

273

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...

274

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Conference Proceedings (OSTI)

The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on ...

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

275

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST  

E-Print Network (OSTI)

Policy Report, over the entire forecast period, primarily because both weather-adjusted peak and commercial sectors. Keywords Electricity demand, electricity consumption, demand forecast, weather normalization, annual peak demand, natural gas demand, self-generation, California Solar Initiative. #12;ii #12

276

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

277

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

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

278

Entity State Code Class of Ownership Residential Commercial...  

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

Code Class of Ownership Residential Commercial Industrial Transportation All Sectors DTE Energy Services AL Non-Utility - - 458,868 - 458,868 Riceland Foods Inc. AR Non-Utility -...

279

Estimated United States Residential Energy Use in 2005  

DOE Green Energy (OSTI)

A flow chart depicting energy flow in the residential sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 11,000 trillion British Thermal Units (trBTUs) of electricity and fuels were used throughout the United States residential sector in lighting, electronics, air conditioning, space heating, water heating, washing appliances, cooking appliances, refrigerators, and other appliances. The residential sector is powered mainly by electricity and natural gas. Other fuels used include petroleum products (fuel oil, liquefied petroleum gas and kerosene), biomass (wood), and on-premises solar, wind, and geothermal energy. The flow patterns represent a comprehensive systems view of energy used within the residential sector.

Smith, C A; Johnson, D M; Simon, A J; Belles, R D

2011-12-12T23:59:59.000Z

280

Calculating Residential Carbon Dioxide Emissions --A New Approach  

E-Print Network (OSTI)

Calculating Residential Carbon Dioxide Emissions -- A New Approach Larry Hughes, Kathleen Bohan to submit an annual national greenhouse gas inventory to the United Nations Framework Convention on Climate different sectors and their associated greenhouse gas emissions (principally carbon dioxide, methane

Hughes, Larry

Note: This page contains sample records for the topic "residential sector forecast" 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

A new hybrid iterative method for short-term wind speed forecasting  

E-Print Network (OSTI)

Electric Load Model (HELM).1 HELM takes many specific end-use forecasts for each sector and applies for electricity. It is driven by detailed forecasts of economic activity, demographic patterns, and alternative of electricity. Demand forecasts both determine, and are determined by, electricity prices. Therefore demand

282

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

283

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

284

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.

285

Evaluation of evolving residential electricity tariffs , Nicholas DeForest o  

E-Print Network (OSTI)

Poster: Evaluation of evolving residential electricity tariffs Judy Lai o , Nicholas DeForest o-130% of baseline) Tier 1 (Baseline) Evaluation of evolving residential electricity tariffs Judy Lai o, Nicholas De sold to the residential sector. Tariffs are colour coded and generally are increasing both through time

286

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...

287

Table 7.6 Coal Stocks by Sector, End of Year 1949-2011 ...  

U.S. Energy Information Administration (EIA)

Table 7.6 Coal Stocks by Sector, End of Year 1949-2011 (Million Short Tons) Year: Producers and Distributors: Consumers: Total: Residential

288

Sector 7  

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

Publications Publications A Reminder for Sector 7 PIs and Users: Please report your new publications to the Sector Manager and the CAT Director. The APS requires PIs to submit new publications to its Publication Database, a link which can be found on the Publication section of the APS web site. Publication information for work done at 7ID Proper acknowledgement sentences to include in papers. Sector 7 Call for APS User Activity Reports. APS User Activity Reports by MHATT-CATers. Recent articles Recent theses Sector 7 Reports Sector 7 Recent research highlights (New) Design documents in ICMS on Sector 7 construction and operation Sector 7 related ICMS documents Library Resources available on the WWW The ANL Library system ANL electronic journal list AIM Find it! Citation Ranking by ISI (see Journal citation report)

289

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

290

Energy End-Use Flow Maps for the Buildings Sector  

SciTech Connect

Graphical presentations of energy flows are widely used within the industrial sector to depict energy production and use. PNNL developed two energy flow maps, one each for the residential and commercial buildings sectors, in response to a need for a clear, concise, graphical depiction of the flows of energy from source to end-use in the building sector.

Belzer, David B.

2006-12-04T23:59:59.000Z

291

Analysis of ultimate energy consumption by sector in Islamic republic of Iran  

Science Conference Proceedings (OSTI)

Total ultimate energy consumption in Iran was 1033.32 MBOE in 2006, and increased at an average annual rate of 6% in 1996-2006. Household and commercial sector has been the main consumer sector (418.47 MBOE) and the fastest-growing sector (7.2%) that ... Keywords: Iran, agricultural sector, energy audits, energy consumption, industrial sector, residential and commercial sector, transportation sector

B. Farahmandpour; I. Nasseri; H. Houri Jafari

2008-02-01T23:59:59.000Z

292

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

293

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

294

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

295

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,

296

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

297

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 ...

298

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

299

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

300

Development of a commercial-sector data base and forecasting model for electricity usage and demand. Volume I. Preliminary model specification. [Description of subprograms BEHAV, DEMAND, ECON, ENER, and INGEN  

SciTech Connect

This is the first of twelve major technical reports under the Commission's contract with Hittman Associates. The contract will lead to the development of a data base on commercial space, and the development of a model to forecast electricity usage and demand. This report presents a preliminary specification of the model to be developed. The model being developed combines econometric and engineering approaches, and consists of five subprograms and an overall executing program. The first subprogram forecasts the stock of commercial space, based on employment data and other economic inputs. It also distinguishes among various types of commercial space, and breaks the commercial space into segments according to fuels for various end uses, such as heating, cooling, etc. The second subprogram uses detailed building-survey data to specify a typical, or characteristic building for each unique type of floorspace considered in the study. The third subprogram calculates monthly electricity usage for the typical buildings specified, using standard engineering techniques, and then scales up the electricity use for each building type according to the amount of space, of that type, in the entire building stock. The fourth subprogram performs a similar function, but produces hourly electricity demands, rather than monthly electricity usage. The fifth, and final subprogram adjusts the energy usage and demand values calculated to simulate the impact of certain economic conditions or policy measures. The report presents a flow chart for each subprogram, and a table of inputs and outputs required for each. The logic, structure, flow, and information transfer of each is described.

1980-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

Transportation Sector Module 1999 Appendix A. Input Data and Parameters, Model Documentation  

Reports and Publications (EIA)

As a component of the National Energy Modeling System integrated forecasting tool, thetransportation model generates mid-term forecasts of transportation sector energy consumption. The transportation model facilitates policy analysis of energy markets, technological development, environmental issues, and regulatory development as they impact transportation sector energy consumption.

John Maples

1999-01-01T23:59:59.000Z

302

Sector 7  

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

Sector 7 : Time Resolved Research Group Sector 7 is operated by the Time Resolved Research Group, which is part of the X-ray Science Division (XSD) of the Advanced Photon Source. Our research focus is the study of Ultrafast fs-laser excitation of matter, using x-ray scattering and spectroscopy techniques. The sector developped two hard x-ray beamlines (7ID and 7BM) focused on time-resolved science. The 7BM beamline has been dedicated for time-resolved radiography of fuel sprays. Sector 7 Links: What's New Beamlines Overview User information: Getting Beamtime Current Research Programs Links to our partners, and collaborators (New) Publications Contact information Operational data (w/ current 7ID schedule) ES&H information (ESAF, EOR, TMS training, User Training)

303

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 Sűrensen

2003-01-01T23:59:59.000Z

304

National Residential Efficiency Measures Database Aimed at Reducing Risk for Residential Retrofit Industry (Fact Sheet)  

DOE Green Energy (OSTI)

This technical highlight describes NREL research to develop a publicly available database of energy retrofit measures containing performance characteristics and cost estimates for nearly 3,000 measures. Researchers at the U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) have developed the National Residential Efficiency Measures Database, a public database that characterizes the performance and costs of common residential energy efficiency measures. The data are available for use in software programs that evaluate cost-effective retrofit measures to improve the energy efficiency of residential buildings. The database provides a single, consistent source of current data for DOE and private-sector energy audit and simulation software tools and the retrofit industry. The database will reduce risk for residential retrofit industry stakeholders by providing a central, publicly vetted source of up-to-date information.

Not Available

2012-01-01T23:59:59.000Z

305

Factors that Influence the Use of Climate Forecasts: Evidence from the 1997/98 El Nińo Event in Peru  

Science Conference Proceedings (OSTI)

This article analyzes the use of climate forecasts among members of the Peruvian fishing sector during the 1997/98 El Nińo event. It focuses on the effect of the time of hearing a forecast on the socioeconomic responses to the forecast. Findings ...

Benjamin S. Orlove; Kenneth Broad; Aaron M. Petty

2004-11-01T23:59:59.000Z

306

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) | Open  

Open Energy Info (EERE)

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Non-renewable Energy Topics: Baseline projection, Co-benefits assessment, GHG inventory Resource Type: Software/modeling tools User Interface: Spreadsheet Complexity/Ease of Use: Simple Website: www.esmap.org/esmap/EFFECT Cost: Free Equivalent URI: www.esmap.org/esmap/EFFECT Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Screenshot

307

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 &...

308

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...

309

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...

310

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...

311

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...

312

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 &...

313

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...

314

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...

315

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...

316

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...

317

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...

318

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...

319

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...

320

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...

Note: This page contains sample records for the topic "residential sector forecast" 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

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...

322

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

323

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

324

Building Technologies Office: Residential Energy Efficiency Stakeholder  

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

Energy Efficiency Stakeholder Meeting - Spring 2012 Energy Efficiency Stakeholder Meeting - Spring 2012 The U.S. Department of Energy (DOE) Building America program held the second annual Residential Energy Efficiency Stakeholder Meeting on February 29-March 2, 2012, in Austin, Texas. At this meeting, hundreds of building industry professionals came together to share their perspective on the most current innovation projects in the residential buildings sector. This meeting provided an opportunity for researchers and industry stakeholders to showcase and discuss the latest in cutting-edge, energy-efficient residential building technologies and practices. The meeting also included working sessions from each Standing Technical Committee (STC), which outlined work that will best assist in overcoming technical challenges and delivering Building America research results to the market. Learn more about the STCs and the research planning process.

325

U.S. Energy Information Administration (EIA) - Sector  

Gasoline and Diesel Fuel Update (EIA)

Residential sector energy demand Residential sector energy demand Residential energy intensity continues to decline across a range of technology assumptions figure data In the AEO2013 Reference case, the energy intensity of residential demand, defined as annual energy use per household, declines from 97.2 million Btu in 2011 to 75.5 million Btu in 2040 (Figure 55). The projected 22-percent decrease in intensity occurs along with a 32-percent increase in the number of homes. Residential energy intensity is affected by various factors-for example, population shifts to warmer and drier climates, improvements in the efficiency of building construction and equipment stock, and the attitudes and behavior of residents toward energy savings. Three alternative cases show the effects of different technology

326

U.S. Energy Information Administration (EIA) - Sector  

Gasoline and Diesel Fuel Update (EIA)

coal Residential coal Residential market trends icon 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. See more issues 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 consumption. The residential sector accounted for 57 percent of that energy

327

U.S. Energy Information Administration (EIA) - Sector  

Gasoline and Diesel Fuel Update (EIA)

Residential sector energy demand Residential sector energy demand Residential energy intensity continues to decline across a range of technology assumptions figure data In the AEO2013 Reference case, the energy intensity of residential demand, defined as annual energy use per household, declines from 97.2 million Btu in 2011 to 75.5 million Btu in 2040 (Figure 55). The projected 22-percent decrease in intensity occurs along with a 32-percent increase in the number of homes. Residential energy intensity is affected by various factors-for example, population shifts to warmer and drier climates, improvements in the efficiency of building construction and equipment stock, and the attitudes and behavior of residents toward energy savings. Three alternative cases show the effects of different technology

328

Sector 7  

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

Link to Sector 7 Users and Collaborators Link to Sector 7 Users and Collaborators This is an incomplete list of Partners from Universities and National Labs who use the facilities at Sector 7. If you wish to add a link to your institutional page, do no hesitate to contact Eric Dufresne at the APS. The APS XSD Atomic, Molecular and Optical Physics group Center for Molecular Movies at Copenhagen University Roy Clarke Group at the University of Michigan Rob Crowell Group at BNL Chris Elles's group at Kansas University Argonne's Transportation Technology R&D Center Fuel Injection and Spray Research Group Paul Evans's group web page at the University of Wisconsin Alexei Grigoriev's group at Univ. of Tulsa Eric Landahl's web page at DePaul University The SLAC Pulse Institute Ultrafast Materials Science group (D. Reis and A. Lindenberg)

329

Sector X  

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

X X If there is an emergency at ETTP requiring evacuation, Sector X reports to the shelter at: Oak Ridge High School 127 Providence Road Oak Ridge, TN 37830 Take most direct route to northbound Bethel Valley Road toward Oak Ridge. Turn left onto Illinois Avenue (Highway 62). Turn right onto Oak Ridge Turnpike and turn left to Oak Ridge High School. If there is an emergency at ORNL requiring evacuation, Sector X reports to the shelter at: Karns High School 2710 Byington Solway Road Knoxville, TN 37931 Take most direct route to northbound Bethel Valley Road toward Knoxville. Then take a left at Highway 62 (Oak Ridge Highway) eastbound to Knoxville. Take a right onto State Route 131 (Byington Beaver Ridge) to Karns High School. If there is an emergency at Y-12 requiring evacuation, Sector X reports to the shelter at:

330

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...

331

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...

332

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...

333

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 &...

334

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

All Reports & Publications All Reports & Publications Search By: Go Pick a date range: From: To: Go graph of U.S. electricity end use, as explained in the article text U.S. electricity sales have decreased in four of the past five years December 20, 2013 Gas furnace efficiency has large implications for residential natural gas use December 5, 2013 EIA publishes state fact sheets on residential energy consumption and characteristics August 19, 2013 All 48 related articles â€ș ResidentialAvailable formats PDF Modeling Distributed Generation in the Buildings Sectors Released: August 29, 2013 This report focuses on how EIA models residential and commercial sector distributed generation, including combined heat and power, for the Annual Energy Outlook. State Fact Sheets on Household Energy Use

335

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

336

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

337

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

338

Sector 7  

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

: News : News Sector 7 calendar of events. APS News APS Monthly meeting slides What's new at the APS Sector 7? 2013 news 2012 news 2011 news 2010 news 2009 news 2008 news 2007 news 2006 news 2005 news 2004 news 2003 news 2002 news 2001 news 2013 News from APS Sector 7 May 2013: Ruben Reininger et al. recently published an article on the optical design of the SPX Imaging and Microscopy beamline (SPXIM). The details can be found on the RSI web site here. A new web page is now available to guide 7-BM users. See the official 7-BM web page for more details. 2012 News from APS Sector 7 August 2012: Jin Wang gave a talk on August 29, 2012 entitled "The APS 7-BM is Open for Business, Officially!" at the August APS Monthly Operation Meeting. On August 1, Alan Kastengren joined the X-ray Science Division to operate the 7-BM beamline. Alan has been involved in the construction

339

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...

340

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

Note: This page contains sample records for the topic "residential sector forecast" 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

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

342

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

343

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

SciTech Connect

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

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

1992-02-01T23:59:59.000Z

344

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

SciTech Connect

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

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

1992-02-01T23:59:59.000Z

345

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

346

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 ...

347

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

348

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...

349

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

350

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

351

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

352

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

353

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

354

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

355

Miscellaneous Electricity Services in the Buildings Sector (released in AEO2007)  

Reports and Publications (EIA)

Residential and commercial electricity consumption for miscellaneous services has grown significantly in recent years and currently accounts for more electricity use than any single major end-use service in either sector (including space heating, space cooling, water heating, and lighting). In the residential sector, a proliferation of consumer electronics and information technology equipment has driven much of the growth. In the commercial sector, telecommunications and network equipment and new advances in medical imaging have contributed to recent growth in miscellaneous electricity use

Information Center

2007-03-11T23:59:59.000Z

356

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

357

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

358

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 Sűrensen

2011-01-01T23:59:59.000Z

359

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

360

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

Note: This page contains sample records for the topic "residential sector forecast" 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

The Effect of Spatial Aggregation on the Skill of Seasonal Precipitation Forecasts  

Science Conference Proceedings (OSTI)

Skillful forecasts of 3-month total precipitation would be useful for decision making in hydrology, agriculture, public health, and other sectors of society. However, with some exceptions, the skill of seasonal precipitation outlooks is modest, ...

Xiaofeng Gong; Anthony G. Barnston; M. Neil Ward

2003-09-01T23:59:59.000Z

362

Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review  

Science Conference Proceedings (OSTI)

Artificial Intelligence (AI) has been used and applied in different sectors, such as engineering, economic, medicine, military, marine, etc. AI has also been applied for modelling, identification, optimisation, prediction, forecasting, and control ... Keywords: AI, FPGA, GAs, VHDL, artificial intelligence, fuzzy logic, genetic algorithms, hybrid systems, neural networks, photovoltaic systems, solar radiation forecasting, solar radiation modelling, solar radiation prediction

Adel Mellit

2008-11-01T23:59:59.000Z

363

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

364

Residential solar home resale analysis  

DOE Green Energy (OSTI)

One of the determinants of the market acceptance of solar technologies in the residential housing sector is the value placed upon the solar property at the time of resale. The resale factor is shown to be an important economic parameter when net benefits of the solar design are considered over a typical ownership cycle rather than the life cycle of the system. Although a study of solar resale in Davis, Ca, indicates that those particular homes have been appreciating in value faster than nonsolar market comparables, no study has been made that would confirm this conclusion for markets in other geograhical locations with supporting tests of statistical significance. The data to undertake such an analysis is available through numerous local sources; however, case by case data collection is prohibitively expensive. A recommended alternative approach is to make use of real estate market data firms who compile large data bases and provide multi-variate statistical analysis packages.

Noll, S.A.

1980-01-01T23:59:59.000Z

365

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

366

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

367

The residential energy map : catalyzing energy efficiency through remote energy assessments and improved data access  

E-Print Network (OSTI)

Although energy efficiency has potential to be a significant energy resource in the United States, many energy efficiency projects continue to go unrealized. This is especially true in the residential sector, where efficiency ...

Howland, Alexis (Alexis Blair)

2013-01-01T23:59:59.000Z

368

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

369

Sector 7  

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

Research Programs Research Programs Sector 7's research program exploits the brilliance of the APS undulator radiation to perform material research studies with high spatial and temporal resolution. Microbeam studies are made using x-ray beam sizes on the submicron-scale, and time-resolved diffraction measurements are carried out with picosecond resolution. Sector 7's undulator line has experimental enclosures dedicated to both time-resolved and microbeam research. In one of these enclosures (7ID-D), a femtosecond laser facility is set up for ultrafast diffraction and spectroscopy studies in a pump-probe geometry. The 7ID-B hutch is a white beam capable station used for time-resolved phase-contrast imaging and beamline optics development. A third enclosure (7ID-C) is instrumented for high-resolution diffraction studies with a Huber 6-circle diffractometer. The instrument is ideal for thin-film and interface studies, including the recently developed Coherent Bragg Rod Analysis (COBRA) technique. The fs-laser has recently been delivered to 7ID-C so time-resolved laser pump-x-ray probe can be performed in 7ID-C since March 2007. An x-ray streak camera is also being commissioned in 7ID-C. 7ID-C is equipped for microdiffraction studies with a small Huber 4-cicle diffractometer used with zone-plate optics.

370

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

371

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.

372

Strategies for Low Carbon Growth In India: Industry and Non Residential  

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

Strategies for Low Carbon Growth In India: Industry and Non Residential Strategies for Low Carbon Growth In India: Industry and Non Residential Sectors Title Strategies for Low Carbon Growth In India: Industry and Non Residential Sectors Publication Type Report Refereed Designation Unknown LBNL Report Number LBNL-4557E Year of Publication 2011 Authors Sathaye, Jayant A., Stephane Rue de la du Can, Maithili Iyer, Michael A. McNeil, Klaas Jan Kramer, Joyashree Roy, Moumita Roy, and Shreya Roy Chowdhury Date Published 5/2011 Publisher LBNL Keywords Buildings Energy Efficiency, CO2 Accounting Methodology, CO2 mitigation, Demand Side Management, energy efficiency, greenhouse gas (ghg), india, industrial energy efficiency, industrial sector, Low Carbon Growth, Low Growth, Non Residential Abstract This report analyzed the potential for increasing energy efficiency and reducing greenhouse gas emissions (GHGs) in the non-residential building and the industrial sectors in India. The first two sections describe the research and analyses supporting the establishment of baseline energy consumption using a bottom up approach for the non residential sector and for the industry sector respectively. The third section covers the explanation of a modeling framework where GHG emissions are projected according to a baseline scenario and alternative scenarios that account for the implementation of cleaner technology.

373

United States energy supply and demand forecasts 1979-1995  

SciTech Connect

Forecasts of U.S. energy supply and demand by fuel type and economic sector, as well as historical background information, are presented. Discussion and results pertaining to the development of current and projected marginal energy costs, and their comparison with market prices, are also presented.

Walton, H.L.

1979-01-01T23:59:59.000Z

374

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...

375

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

376

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

377

Property:Incentive/ImplSector | Open Energy Information  

Open Energy Info (EERE)

ImplSector ImplSector Jump to: navigation, search Property Name Incentive/ImplSector Property Type String Description Implementing Sector. Pages using the property "Incentive/ImplSector" Showing 25 pages using this property. (previous 25) (next 25) 2 2003 Climate Change Fuel Cell Buy-Down Program (Federal) + Federal + 3 30% Business Tax Credit for Solar (Vermont) + State/Territory + 4 401 Certification (Vermont) + State/Province + A AEP (Central and North) - CitySmart Program (Texas) + Utility + AEP (Central and North) - Residential Energy Efficiency Programs (Texas) + Utility + AEP (Central and SWEPCO) - Coolsaver A/C Tune Up (Texas) + Utility + AEP (Central, North and SWEPCO) - Commercial Solutions Program (Texas) + Utility + AEP (SWEPCO) - Residential Energy Efficiency Programs (Texas) + Utility +

378

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...

379

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...

380

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...

Note: This page contains sample records for the topic "residential sector forecast" 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

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...

382

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...

383

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

384

Lighting in Residential and Commercial Buildings (1993 and 1995 Data) --  

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

Commercial Buildings Home > Special Topics and Data Reports > Types of Lights Commercial Buildings Home > Special Topics and Data Reports > Types of Lights Picture of a light bulb At Home and At Work: What Types of Lights Are We Using? Two national EIA surveys report that . . . Of residential households, 98 percent use incandescent, 42 percent use fluorescent. Of commercial buildings, 59 percent use incandescent, 92 percent use fluorescent. At a glance, we might conclude that substantial energy savings could occur in both the residential and commercial sectors if they replaced their incandescent lights with fluorescent lights, given that fluorescent lights consume approximately 75-85 percent less electricity than incandescent lights. In the residential sector, this is true. However, in the commercial sector, where approximately 92 percent of the buildings already use fluorescent lights, increasing energy savings will require upgrading existing lights and lighting systems. To maximize energy savings, analysis must also consider the hours the lights are used and the amount of floorspace lit by that lighting type. Figures 1 and 2 show the types of lights used by the percent of households and by the percent of floorspace lit for the residential and the commercial sectors, respectively.

385

Testing share & load growth in competitive residential gas markets  

SciTech Connect

The residential market stands as the next frontier for natural gas unbundling. In California, Illinois, Maryland, Massachusetts, New Jersey, New York, Ohio, Pennsylvania and elsewhere, states have introduced pilot programs and other unbundling efforts to target residential gas consumers. These efforts are hardly surprising. The residential market, presently dominated by the regulated local distribution companies, appears lucrative. In 1995, the residential sector of the U.S. natural gas industry consumed 4,736 trillion Btu of natural gas or 32 percent of all natural gas delivered by LDCs in that year. U.S. residential consumers accounted for $28.7 billion or 59 percent of the gas utility industry`s total revenues. Nevertheless, despite all the enthusiasm industry representatives have recently expressed in trade publications and public forums, the creation of a competitive residential market may prove a very slow process. Marketers appear cautious in taking the responsibility of serving residential consumers, and for very good reasons. Gaining a sizable portion of this market requires substantial investment in mass marketing, development of name recognition, acquisition of appropriate technology and employment of skillful personnel. Moreover, residential customers do not behave rationally in a {open_quotes}neoclassical{close_quotes} economic sense. They react not only to a price but to several qualitative factors that have yet to be studied by LDCs and marketers. This article offers results from creating a software program and model that answer two basic questions: (1) What share of the residential natural gas market can be realistically captured by non-regulated suppliers? (2) Will residential unbundling increase total throughput for gas utilities? If so, by how much?

Lonshteyn, A. [Boston Gas Company, MA (United States)

1998-02-15T23:59:59.000Z

386

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

387

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

388

Assessment of Industrial-Sector Load Shapes  

Science Conference Proceedings (OSTI)

The load shapes of industrial-sector customers are becoming increasingly important for utility forecasting, marketing, and demand-side management planning and evaluation activities. This report analyzes load shapes for various industry segments and investigates the transfer of these load shapes across service territories. This report is available only to funders of Program 101A or 101.001. Funders may download this report at http://my.primen.com/Applications/DE/Community/index.asp .

1993-02-18T23:59:59.000Z

389

Material World: Forecasting Household Appliance Ownership in a Growing Global Economy  

SciTech Connect

Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

Letschert, Virginie; McNeil, Michael A.

2009-03-23T23:59:59.000Z

390

Residential Transportation Historical Publications reports, data and  

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

Historical Publications Historical Publications Residential Transportation reports, data tables and transportation questionnaires Released: May 2008 The Energy Information Administration conducts several core consumption surveys. Among them was the Residential Transportation Energy Consumption Survey (RTECS). RTECS was designed by EIA to provide information on how energy is used by households for personal vehicles. It was an integral part of a series of surveys (i.e., core consumption surveys) designed by EIA to collect data on energy used by end-use economic sectors. The RTECS collected data on the number and type of vehicles used by the household. For each vehicle, data were collected on the number of miles traveled (commonly called VMT) for the year, the number of gallons of fuel consumed, the type of fuel used, the priced paid for fuel, and the number of miles per gallon. Additional electronic releases are available on the Transportation homepage.

391

Building Technologies Office: Residential Building Activities  

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

Building Activities Building Activities The Department of Energy (DOE) is leading several different activities to develop, demonstrate, and deploy cost-effective solutions to reduce energy consumption across the residential building sector by at least 50%. The U.S. DOE Solar Decathlon is a biennial contest which challenges college teams to design and build energy efficient houses powered by the sun. Each team competes in 10 contests designed to gauge the performance, livability and affordability of their house. The Building America program develops market-ready energy solutions that improve the efficiency of new and existing homes while increasing comfort, safety, and durability. Guidelines for Home Energy Professionals foster the growth of a high quality residential energy upgrade industry and a skilled and credentialed workforce.

392

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.

393

Sector 7  

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

User Information & Getting Beamtime User Information & Getting Beamtime There are three ways to request beamtime to perform an experiment on APS-7ID. One can request beam time as an APS General User, as an APS Partner User, or one can contact a staff member of Sector 7 to work collaboratively with them using a small amount of staff time to gather preliminary data. 80% of the available beamtime on 7ID is given to General and Partner Users, while 20% is reserved for staff use. Beam time is allocated and announced by email shortly before the start of an experimental run. In October 2002, beamline 7ID welcomed its first APS General Users (GU). To gain access to 7ID, General or Partner Users are required to submit a proposal to the APS GU Website by the specified deadline. Sucessful proposals will be scheduled for the next cycle following the proposal deadline. There are three proposal cycles per year with deadlines about two months before the start of a run. The deadlines and General User forms are available on the web through the APS General User Web site. Specific instructions for new General Users are available on the site. These instructions can be helpful also for new APS Users in general.

394

Sector 7  

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

Overview and History Overview and History Sector 7 consists of two APS beamlines: 7-ID: an insertion device beamline based on an APS Type-A Undulator 7-BM: a bend magnet beam line for time-resolved radiography (currently being commissioned) Overview of 7-ID 7-ID comprises four large experimental enclosures designated A, B, C, and D. In 2004, a laser enclosure was also added (7ID-E). Enclosure 7-ID-A is the first optics enclosure and houses a polished Be window, an empty x-ray filter unit, a pair of white beam slits, a water-cooled double crystal diamond monochromator (Kohzu HLD4), and a P4 mode shutter. The beamline vertical offset is 35 mm. Enclosure 7-ID-B is a white-, or monochromatic-beam experimental enclosure. It is equipped with two precision motorized table for alignment and positioning of experimental equipment. This station is used for white-beam imaging or microdiffraction experiments.

395

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 &...

396

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...

397

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...

398

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...

399

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

400

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

Note: This page contains sample records for the topic "residential sector forecast" 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

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

402

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

403

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

404

Hawaii demand-side management resource assessment. Final report, Reference Volume 2: Final residential and commercial building prototypes and DOE-2.1E developed UECs and EUIs; Part 2  

SciTech Connect

This section contains the detailed measured impact results and market segment data for each DSM case examined for this building type. A complete index of all base and measure cases defined for this building type is shown first. This index represents an expansion of the base and measure matrix presented in Table 1 (residential) or Table 2 (commercial) for the applicable sector. Following this index, a summary report sheet is provided for each DSM measure case in the order shown in the index. The summary report sheet contains a host of information and selected graphs which define and depict the measure impacts and outline the market segment data assumptions utilized for each case in the DBEDT DSM Forecasting models. The variables and figures included in the summary report sheet are described. Numerous tables and figures are included.

NONE

1995-04-01T23:59:59.000Z

405

Large-Scale Residential Energy Efficiency Programs Based on CFLs | Open  

Open Energy Info (EERE)

Large-Scale Residential Energy Efficiency Programs Based on CFLs Large-Scale Residential Energy Efficiency Programs Based on CFLs Jump to: navigation, search Tool Summary Name: Large-Scale Residential Energy Efficiency Programs Based on CFLs Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Energy Efficiency, Buildings Topics: Implementation, Policies/deployment programs Website: www.esmap.org/filez/pubs/216201021421_CFL_Toolkit_Web_Version_021610_R References: Large-Scale Residential Energy Efficiency Programs Based on CFLs[1] Overview "The World Bank Group and its Energy Sector Management Assitance Progamme (ESMAP) have produced a toolkit for efficient lighting programmes, based on compact fluorescent lamps, that compiles and shares operational (design,

406

Figure 7. U.S. dry natural gas consumption by sector, 2005-2040 ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 7. U.S. dry natural gas consumption by sector, 2005-2040 (trllion cubic feet) Residential Commercial Transportation Gas to liquids

407

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

7 7 Characteristics of a Typical Single-Family Home (1) Year Built | Building Equipment Fuel Age (5) Occupants 3 | Space Heating Natural Gas 12 Floorspace | Water Heating Natural Gas 8 Heated Floorspace (SF) 1,934 | Space Cooling 8 Cooled Floorspace (SF) 1,495 | Garage 2-Car | Stories 1 | Appliances Size Age (5) Foundation Concrete Slab | Refrigerator 19 Cubic Feet 8 Total Rooms (2) 6 | Clothes Dryer Bedrooms 3 | Clothes Washer Other Rooms 3 | Range/Oven Full Bathroom 2 | Microwave Oven Half Bathroom 0 | Dishwasher Windows | Color Televisions 3 Area (3) 222 | Ceiling Fans 3 Number (4) 15 | Computer 2 Type Double-Pane | Printer Insulation: Well or Adequate | Note(s): Source(s): 2-Door Top and Bottom Electric Top-Loading Electric 1) This is a weighted-average house that has combined characteristics of the Nation's stock homes. Although the population of homes with

408

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

9 9 Average Annual Energy Expenditures per Household, by Year ($2010) Year 1980 1,991 1981 1,981 1982 2,058 1983 2,082 1984 2,067 1985 2,012 1986 1,898 1987 1,846 1988 1,849 1989 1,848 1990 1,785 1991 1,784 1992 1,729 1993 1,797 1994 1,772 1995 1,727 1996 1,800 1997 1,761 1998 1,676 1999 1,659 2000 1,824 2001 1,900 2002 1,830 2003 1,978 2004 2,018 2005 2,175 2006 2,184 2007 2,230 2008 2,347 2009 2,173 2010 2,201 2011 2,185 2012 2,123 2013 2,056 2014 2,032 2015 2,030 2016 2,007 2017 1,992 2018 1,982 2019 1,973 2020 1,963 2021 1,961 2022 1,964 2023 1,962 2024 1,959 2025 1,957 2026 1,959 2027 1,960 2028 1,953 2029 1,938 2030 1,932 2031 1,937 2032 1,946 2033 1,956 2034 1,967 2035 1,978 Source(s): Average Expenditure EIA, State Energy Data 2009: Prices and Expenditures, Jun. 2011 for 1980-2009; EIA, Annual Energy Outlook 2012 Early Release, Jan. 2012, Table A2, p. 3-

409

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

8 8 Presence of Air-Conditioning and Type of Heating System in New Single-Family Homes Type of Primary Heating System Warm-Air Hot Water Other or | Furnace Heat Pump or Steam (1) None (2) | 1980 57% 24% 4% 15% | 62% 1981 56% 25% 3% 16% | 65% 1982 53% 26% 4% 17% | 66% 1983 56% 29% 4% 12% | 69% 1984 55% 30% 4% 11% | 71% 1985 54% 30% 5% 11% | 70% 1986 54% 29% 7% 10% | 69% 1987 57% 27% 7% 9% | 71% 1988 60% 26% 7% 8% | 75% 1989 63% 24% 6% 7% | 77% 1990 64% 23% 6% 6% | 76% 1991 65% 22% 6% 7% | 75% 1992 66% 24% 6% 5% | 77% 1993 67% 24% 5% 5% | 78% 1994 67% 24% 5% 4% | 79% 1995 66% 25% 5% 4% | 79% 1996 70% 23% 5% 2% | 81% 1997 70% 23% 5% 2% | 82% 1998 72% 21% 4% 3% | 83% 1999 72% 22% 4% 2% | 84% 2000 71% 23% 4% 2% | 85% 2001 71% 23% 4% 1% | 86% 2002 71% 23% 4% 2% | 87% 2003 71% 24% 3% 2% | 88% 2004 70% 26% 3% 1% | 90% 2005 67% 29% 3% 1% | 89% 2006 63% 33% 3% 2% | 89% 2007 62% 34% 2% 2% | 90% 2008 60% 34% 3% 3% | 89% 2009 56% 37% 3% 4% | 88% 2010 56% 38% 2% 3% | 88% Note(s) Source(s):

410

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

1 1 Total Number of Households and Buildings, Floorspace, and Household Size, by Year 1980 80 N.A. 227 2.9 1981 83 N.A. 229 2.8 1982 84 N.A. 232 2.8 1983 85 N.A. 234 2.8 1984 86 N.A. 236 2.7 1985 88 N.A. 238 2.7 1986 89 N.A. 240 2.7 1987 91 N.A. 242 2.7 1988 92 N.A. 244 2.7 1989 93 N.A. 247 2.6 1990 94 N.A. 250 2.6 1991 95 N.A. 253 2.7 1992 96 N.A. 257 2.7 1993 98 N.A. 260 2.7 1994 99 N.A. 263 2.7 1995 100 N.A. 266 2.7 1996 101 N.A. 269 2.7 1997 102 N.A. 273 2.7 1998 104 N.A. 276 2.7 1999 105 N.A. 279 2.7 2000 106 N.A. 282 2.7 2001 107 2% 285 2.7 2002 105 3% 288 2.7 2003 106 5% 290 2.8 2004 107 7% 293 2.7 2005 109 9% 296 2.7 2006 110 11% 299 2.7 2007 110 12% 302 2.7 2008 111 13% 304 2.8 2009 111 13% 307 2.8 2010 114 14% 310 2.7 2011 115 14% 313 2.7 2012 116 15% 316 2.7 2013 117 16% 319 2.7 2014 118 17% 322 2.7 2015 119 18% 326 2.7 2016 120 19% 329 2.7 2017 122 21% 332 2.7 2018 123 22% 335 2.7 2019 125 23% 338 2.7 2020 126 25% 341 2.7 2021 127 26% 345

411

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

customer groups. While the cost per kWh for each respectivewith the average cost declines, per kWh for average andcost of doing so would be zero (prior to 2011), or small, on the order of 5 cents per kWh (

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

412

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

of window) Heating Cooling Conduction Solar Total Conductionsolarload = heating or cooling load from solar gain throughsolar usability to account for its deacreasing effectiveness to offset heating

Wenzel, T.P.

2010-01-01T23:59:59.000Z

413

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

Energy Rating System (HERS) may provide such actionable market information.market is administered by the Western Region Renewable Energy Generation Information

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

414

ENERGY DATA SOURCEBOOK FOR THE U.S. RESIDENTIAL SECTOR  

E-Print Network (OSTI)

of the Market Technologies Assessment Group in the Energy Analysis Program at LBNL for their time spent. Koomey, Gregory J. Rosenquist, Marla Sanchez, and James W. Hanford September 1997 Energy Analysis Program these input data into a single location. The data provided include information on end-use unit energy

415

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

of stocks, UECs, and national energy consumption for theseSanchez 1997). National energy consumption of these end-usesUECs, and National Energy Consumption of Miscellaneous

Wenzel, T.P.

2010-01-01T23:59:59.000Z

416

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

classes of new appliances; and a large database of stockrecords for those appliances in the database. UECs for otheron the UEC database. For new appliances entering the market,

Wenzel, T.P.

2010-01-01T23:59:59.000Z

417

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

project with both efficiency and solar may be the optimal solution for some customers—and the one that costs

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

418

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

was largely overcome, and PV prices globally had begun toNemet 2006; Nemet 2007). PV price reduction is one of theand indeed the global price of PV modules is a central part

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

419

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

Renewable portfolio standards and cost-effective energy-for low-cost financing for renewable energy and energycost of renewable onsite generation systems and energy

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

420

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

of offering NEM for biogas-electric systems and fuel cells.but AB 2228 (2002) allowed biogas-electric facilities up to

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

Buildings Energy Data Book: 2.2 Residential Sector Characteristics  

Buildings Energy Data Book (EERE)

| 3,680 1,047 1,425 111.1 2,838 941 1,062 Note(s): Source(s): Total U.S. Homes (millions) U.S. Average 1) Average home sizes include both heated and unheated floor space, including...

422

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

typical new buildings is even more difficult than for existing buildings since there are few data on the energy usage

Wenzel, T.P.

2010-01-01T23:59:59.000Z

423

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Appliances and Space Conditioning Equipment. Arthur D.Efficiency standards for space conditioning equipment wereprogram estimates of space conditioning energy use and

Wenzel, T.P.

2010-01-01T23:59:59.000Z

424

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

Energy and Buildings Herring, H. and R. Roy (2007). "quality of energy service” (Herring and Roy 2007: 195). TheEkins et al. 2007: 4935-36; Herring and Roy 2007: 196). As

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

425

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

respond to having a rooftop solar system. There is a robustindustry, since a small rooftop solar system can producecompliance. 27 . Each kW of rooftop solar capacity produces

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

426

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Data, Washington, DC: Energy Information Administration.Model Documentation, Washington, DC: Energy InformationData, Washington, DC: Energy Information Administration.

Wenzel, T.P.

2010-01-01T23:59:59.000Z

427

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

impact of rate design and net metering on the bill savingsJ. , K. Fox, et al. (2009). Net Metering & Interconnectionsmetering (NEM, or “net metering”), to which we turn next. By

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

428

Table 2.1b Residential Sector Energy Consumption Estimates ...  

U.S. Energy Information Administration (EIA)

R=Revised. P=Preliminary. NA=Not available. 4 Based on petroleum product supplied. For petroleum, product supplied is used as an approximation of

429

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

installation Total Electricity Consumption 1 Year Pre & PostGWh total Total Electricity Consumption 1 Year Pre & 2 YearsInstall Total Electricity Consumption 1 Year Pre & 3 Years

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

430

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

an Energy-Efficient Economy. Hanford, J.W. and Y . J. Huang.Laboratory. LBL-33101. Hanford, J.W. , J.G. Koomey, L.E.97. Ritschard, R. L. , J.W. Hanford, and A.O. Sezgen. 1992a.

Wenzel, T.P.

2010-01-01T23:59:59.000Z

431

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

USE DATA 10.1. Baseline Lighting Usage 10.2. Distribution of1996), which monitored lighting usage using light loggersreplacements. Baseline Lighting Usage We divide the current

Wenzel, T.P.

2010-01-01T23:59:59.000Z

432

Modeling diffusion of electrical appliances in the residential sector  

E-Print Network (OSTI)

and usage patterns, and because data sources covering these parameters are more scarce, modeling of household lighting

McNeil, Michael A.

2010-01-01T23:59:59.000Z

433

Building Integrated Photovoltaics (BIPV) in the Residential Sector...  

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

For more than 30 years, there have been strong efforts to accelerate the deployment of solar- electric systems by developing photovoltaic (PV) products that are fully integrated...

434

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

referents (MPRs) for non-renewable energy that serve asPolicies to promote non-hydro renewable energy in the United

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

435

Modeling diffusion of electrical appliances in the residential sector  

E-Print Network (OSTI)

Henderson (2005). Home air conditioning in Europe – how muchA. Pavlova (2003). "Air conditioning market saturation andevidence suggests that air conditioning could be quite an

McNeil, Michael A.

2010-01-01T23:59:59.000Z

436

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

IL: AHAM. The Air Conditioning, Heating and RefrigerationArlington, VA: ARI. ARI, Air-Conditioning and RefrigerationRefrigeration, and Air-conditioning Engineers (ASHRAE).

Wenzel, T.P.

2010-01-01T23:59:59.000Z

437

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Energy kWh/cycle Total Energy Annual Usage kWh/yr Motor +Energy kWh/cycle Total Energy Annual Usage kWb/yr Motortotal incandescent lighting energy consumption attributable to each usage

Wenzel, T.P.

2010-01-01T23:59:59.000Z

438

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

10 1.5. The Coordination of Solar and Energyintegration of solar and energy efficiency. Currentlytension between solar and energy efficiency remains much

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

439

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

meteorological year (TMY) solar radiation data. The goaleither TMY or actual solar radiation data, and thus servesmodeling (using actual solar radiation data, though this

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

440

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

capture such savings: the solar provider has unique pricingscale solar industry. Solar providers will need both to

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "residential sector forecast" 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

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Heating Equipment, Mobile Home Furnaces, Kitchen Ranges and Ovens,oven only range only GDR all gas dryers Gas Dryer cycle data only Gas HeatingHeating Waterbed Heaters Automatic Drip Coffeemaker Crankcase Heater Iron Spa/Hot Tub Electric Blankets Toaster Hair Dryer Toaster Oven

Wenzel, T.P.

2010-01-01T23:59:59.000Z

442

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

U.S. energy-related carbon-dioxide emissions, including both direct fuel consumption (primarily natural gas)

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

443

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

in RECS with utility sub-metering data have found that RECSof sources, including sub-metering of individual appliances,sources including sub-metering of individual appliances,

Wenzel, T.P.

2010-01-01T23:59:59.000Z

444

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

South Gas LPG Oil Electricity Source: US DOE 1995 forLPG -- FRN RM - Oil - OTH Other Source: US DOE 1995b. OilLPG H20 FRN - Oil« RM OTH Other Source: US DOE 1995b. Oil

Wenzel, T.P.

2010-01-01T23:59:59.000Z

445

Greening the Residential Sector: Efforts to Transform the Homebuilding...  

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

with EETD scientists on cooperative research? Get a job in EETD? Make my home more energy-efficient? Find a source within EETD for a news story I'm writing, shooting, or...

446

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

fit gives R- squared of 0.983. Standby losses (usage at zerowater in the tank); and standby losses (the constant lossesof hot water delivered + Standby losses) Total energy used

Wenzel, T.P.

2010-01-01T23:59:59.000Z

447

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

kerosene. Other heating fuel is primarily wood. H20 = steamkerosene. Other heating fuel is primarily wood. H20 = steamkerosene. Other heating fuel is primarily wood. FRN =

Wenzel, T.P.

2010-01-01T23:59:59.000Z

448

Buildings Energy Data Book: 2.3 Residential Sector Expenditures  

Buildings Energy Data Book (EERE)

West National Space Heating 1,050 721 371 352 575 Air-Conditioning 199 175 456 262 311 Water Heating 373 294 313 318 320 Refrigerators 194 145 146 154 157 Other Appliances and...

449

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

renewable energy; and calculating market price referents (Market price referent Net excess generation Net energy

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

450

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

non-low- income electricity bill, according to specificsto offset any future electricity bills. All systems withinunderstand their electricity bills, even if early adopters

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

451

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

Market Shares 6.5. Standards 7. DISHWASHER END-USE DATA 7.1.Dishwasher UECs 7.2.Dishwasher Usage 7.3. Dishwasher Technology Data 7.4. Market

Wenzel, T.P.

2010-01-01T23:59:59.000Z

452

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

appliance hot water usage and electric heating UECs. The hotElectricity Usage, prepared for Virginia Electric Power Co.hot water usage of the dishwasher, calculated using electric

Wenzel, T.P.

2010-01-01T23:59:59.000Z

453

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

terms of age, building size, and insulation levels, we alsoshowed that insulation levels for pre-1980 buildings werebuildings, based on combinations of roof and wall insulation

Wenzel, T.P.

2010-01-01T23:59:59.000Z

454

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network (OSTI)

energy consumption (UECs) of appliances and equipment; Historical and current appliance and equipment market shares; Appliance and equipment efficiency and sales trends;energy consumption (UEC) values of appliances and equipment; historical and current appliance and equipment market shares; appliance and equipment efficiency and sales trends;

Wenzel, T.P.

2010-01-01T23:59:59.000Z

455

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

actual solar radiation and other necessary weather dataSolar 71 Table 5.2. 10x10km Weathersolar energy is actually generated; this makes intuitive sense as edge effects such as shading and weather

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

456

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network (OSTI)

59. City of San Diego and California Center for SustainablePOLICIES AND FUNDING FOR THE CALIFORNIA SOLAR INITIATIVE.San Francisco, California Public Utilities Commission: 44.

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

457

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

458

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

459

Simulation of the GHG Abatement Potentials in the U.S. Building Sector by 2050  

SciTech Connect

Given the substantial contribution of the U.S. building sector to national carbon emissions, it is clear that to address properly the issue of climate change, one must first consider innovative approaches to understanding and encouraging the introduction of new, low-carbon technologies to both the commercial and residential building markets. This is the motivation behind the development of the Stochastic Lite Building Module (SLBM), a long range, open source model to forecast the impact of policy decisions and consumer behavior on the market penetration of both existing and emerging building technologies and the resulting carbon savings. The SLBM, developed at Lawrence Berkeley National Laboratory (LBNL), is part of the Stochastic Energy Deployment System (SEDS) project, a multi-laboratory effort undertaken in conjunction with the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL) and private companies. The primary purpose of SEDS is to track the performance of different U.S. Department of Energy (USDOE) Research and Development (R&D) activities on technology adoption, overall energy efficiency, and CO{sub 2} reductions throughout the whole of the U.S. economy. The tool is fundamentally an engineering-economic model with a number of characteristics to distinguish it from existing energy forecasting models. SEDS has been written explicitly to incorporate uncertainty in its inputs leading to uncertainty bounds on the subsequent forecasts. It considers also passive building systems and their interactions with other building service enduses, including the cost savings for heating, cooling, and lighting due to different building shell/window options. Such savings can be compared with investments costs in order to model real-world consumer behavior and forecast adoption rates. The core objective of this paper is to report on the new window and shell features of SLBM and to show the implications of various USDOE research funding scenarios on the adoption of these and other building energy technologies. The results demonstrate that passive technologies contain significant potential for carbon reductions - exceeding 1165 Mt cumulative savings between 2005 and 2050 (with 50% likelihood) and outperforming similar R&D funding programs for distributed photovoltaics and high efficiency solid-state lighting.

Stadler, Michael; DeForest, Nicholas; Marnay, Chris; Bonnet, Florence; Lai, Judy; Phan, Trucy

2010-10-01T23:59:59.000Z

460

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,

Note: This page contains sample records for the topic "residential sector forecast" 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

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

462

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

463

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).

464

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

465

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

466

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

467

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

468

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

469

Fuel choice and aggregate energy demand in the commercial sector  

SciTech Connect

This report presents a fuel choice and aggregate-demand model of energy use in the commercial sector of the United States. The model structure is dynamic with short-run fuel-price responses estimated to be close to those of the residential sector. Of the three fuels analyzed, electricity consumption exhibits a greater response to its own price than either natural gas or fuel oil. In addition, electricity price increases have the largest effect on end-use energy conservation in the commercial sector. An improved commercial energy-use data base is developed which removes the residential portion of electricity and natural gas use that traditional energy-consumption data sources assign to the commercial sector. In addition, household and commercial petroleum use is differentiated on a state-by-state basis.

Cohn, S.

1978-12-01T23:59:59.000Z

470

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...

471

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...

472

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...

473

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...

474

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...

475

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...

476

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...

477

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...

478

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...

479

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...

480

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 <...

Note: This page contains sample records for the topic "residential sector forecast" 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

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...

482

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...

483

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...

484

Can Climate Forecasts for the Growing Season be Valuable to Crop Producers: Some General Considerations and an Illinois Pilot Study  

Science Conference Proceedings (OSTI)

A three-step process is proposed to be most efficient for generating skillful climate forecasts which could reduce the adverse socioeconomic effects of climatic variability. These steps involve identifying weather-sensitive economic sectors, ...

Steven T. Sonka; Peter J. Lamb; Stanley A. Changnon Jr.; Aree Wiboonpongse

1982-04-01T23:59:59.000Z

485

Transportation Sector Module 1995 - Model Developer's Report, Model Documentation  

Reports and Publications (EIA)

As the description in Section 4 and Appendix B shows, the NEMS Transportation Model is made up of seven semi-independent submodules which address different vehicular modes of the transportation sector. Each submodule also contains methods to deal with the impacts of policyinitiatives and legislative mandates which affect individual modes of travel. The transportation sector energy consumption is the sum of the energy consumption forecasts generated through the separate submodules.

John Maples

1995-03-01T23:59:59.000Z

486

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

487

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

488

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

489

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

490

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

491

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

492

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.

493

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

494

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

495

Sector 30 - useful links  

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

Useful Links Sector 30 Sector Orientation Form HERIX experiment header for lab book MERIX experiment header for lab book Printing from your laptop at the beamline Other IXS sectors...

496

Genesis and legacy : a study of traditional, contemporary and proposed systems of control over residential developments in Cairo, Egypt  

E-Print Network (OSTI)

This thesis deals with contemporary residential developments presently being carried out by the formal private sector in Cairo. These developments are typical of many other cities in Egypt, and indeed throughout the ...

El-Husseiny, Mohamed A. (Mohamed Ahmed)

1987-01-01T23:59:59.000Z

497

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

498

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

499

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

500

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