The requirements of the Government Performance and Results Act (GPRA) of 1993 mandate the reporting of outcomes expected to result from programs of the federal government. The U.S. Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy (EERE) develops official metrics for its 11 major programs using its Office of Planning, Budget Formulation, and Analysis (OPBFA). OPBFA conducts an annual integrated modeling analysis to produce estimates of the energy, environmental, and financial benefits expected from EERE's budget request. Two of EERE's major programs include the Building Technologies Program (BT) and Office of Weatherization and Intergovernmental Program (WIP). Pacific Northwest National Laboratory (PNNL) supports the OPBFA effort by developing the program characterizations and other market information affecting these programs that are necessary to provide input to the EERE integrated modeling analysis. Throughout the report we refer to these programs as "buildings-related" programs because the approach is not limited in application to BT or WIP. To adequately support OPBFA in the development of official GPRA metrics, PNNL communicates with the various activities and projects in BT and WIP to determine how best to characterize their activities planned for the upcoming budget request. PNNL then analyzes these projects to determine what the results of the characterizations would imply for energy markets, technology markets, and consumer behavior. This is accomplished by developing nonintegrated estimates of energy, environmental, and financial benefits (i.e., outcomes) of the technologies and practices expected to result from the budget request. These characterizations and nonintegrated modeling results are provided to OPBFA as inputs to the official benefits estimates developed for the federal budget. This report documents the approach and methodology used to estimate future energy, environmental, and financial benefits produced by technologies and practices supported by BT and by WIP. However, the approach is general enough for analysis of buildings-related technologies, independent of any specific program. An overview describes the GPRA process and the models used to estimate energy savings. The body of the document describes the algorithms used and the diffusion curve estimates.
Residential buildings are a key driver of energy consumption and also impact transportation and land-use. Energy consumption in the residential sector accounts for one-fifth of total U.S. energy consumption and energy-related CO2 emissions, with floor space a major driver of building energy demands. In this work a consistent, vintage-disaggregated, annual long-term series of U.S. housing stock and residential floor space for 1891–2010 is presented. An attempt was made to minimize the effects of the incompleteness and inconsistencies present in the national housing survey data. Over the 1891–2010 period, floor space increased almost tenfold, from approximately 24,700 to 235,150 million square feet, corresponding to a doubling of floor space per capita from approximately 400 to 800 square feet. While population increased five times over the period, a 50% decrease in household size contributed towards a tenfold increase in the number of housing units and floor space, while average floor space per unit remains surprisingly constant, as a result of housing retirement dynamics. In the last 30 years, however, these trends appear to be changing, as household size shows signs of leveling off, or even increasing again, while average floor space per unit has been increasing. GDP and total floor space show a remarkably constant growth trend over the period and total residential sector primary energy consumption and floor space show a similar growth trend over the last 60 years, decoupling only within the last decade.
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ph: (865) 576-8401, fax: (865) 576-5728 email: reports@adonis.osti.gov Available to the public from the National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Rd., Springfield, VA 22161 ph: (800) 553-6847, fax: (703) 605-6900 email: orders@ntis.fedworld.gov online ordering: http://www.ntis.gov/ordering.htmThis document was printed on recycled paper. SummaryThe goal of DOE's Zero-Net Energy Commercial Building Initiative (CBI) is to develop marketable Zero-Net Energy Commercial Buildings, buildings that use cutting-edge efficiency technologies and on-site renewable energy generation to offset their energy use from the electricity grid by 2025. While the impact on commercial energy use in the long term may be substantial from this initiative, over the near term the potential to reduce energy consumption in existing buildings may be more important. The U.S. Department of Energy requested Pacific Northwest National Laboratory to review recent literature as it applied to state and utility efforts to reduce energy use in existing commercial buildings, as a means of helping to define programmatic activities at the federal level. PNNL reviewed six different studies from states all across the U.S. and found that:This review also extended to a report on building monitoring and controls and the potential to improve energy efficiency of existing buildings. However, as a whole, the state and utility studies reviewed here placed little emphasis on the potential for this technology. In large part, this likely stems from the complexity in trying to define an incentive program that would promote adoption of such systems. It is estimated that building sensors and controls-excluding those associated with lighting--have the potential of reducing commercial building energy use by an additional 5 to 20 percent. However, these savings are difficult to distinguish with general improved management and operations or commissioning efforts.
This report describes a comprehensive system of energy intensity indicators for the United States that has been developed for the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) over the past decade. This system of indicators is hierarchical in nature, beginning with detailed indexes of energy intensity for various sectors of the economy, which are ultimately aggregated to an overall energy intensity index for the economy as a whole. The aggregation of energy intensity indexes to higher levels in the hierarchy is performed with a version of the Log Mean Divisia index (LMDI) method. Based upon the data and methods in the system of indicators, the economy-wide energy intensity index shows a decline of about 16% in 2014 relative to a 1985 base year. Discussion of energy intensity indicators for each of the broad end-use sectors of the economy-residential, commercial, industrial, and transportation-is presented in the report. An analysis of recent changes in the efficiency of electricity generation in the U.S. is also included. A detailed appendix describes the data sources and methodology behind the energy intensity indicators for each sector. viii Table S.2. Listing of major data sources
Portions of thisin the study combines elements of engineering simulations and statistical analysis to estimate end-use intensities for heating, cooling, ventilation, lighting, refrigeration, hot water, cooking, and miscellaneous equipment.Adjusted Engineering (SAE) models were estimated by building type. r SAE models used variables such as building size, vintage, on, weekly operating hours, and employee density to adjust the model predicted loads to the observed consumption (based upon ing information). 6751 buildings in the 1992 CBECS. This report describes anThe methodology used End-use consumption by fuel was estimated for The report displays the summary 11 separate building types as well as for the total U.S. commercial building stock.i i i SUMMARY An accurate picture o f how energy is used in the nation's stock of commercial buildings can serve a variety of program planning and policy needs of the U.S. Department of Energy, utilities, and other groups seeking to improve the efficiency of energy use in the building sector. estimations of energy consumption by end use--heating, cooling, 1 ighting, hot water, etc., --based on data from the 1992 Commerci a1 Bui 1 ding Energy Consumption Survey (CBECS). This work was conducted by Pacific Northwest National Laboratory (PNNL) for the Energy End Use and Integrated Statistics Division (EEUISD) within the Energy Information Administration (EIA). generated estimates of end-use consumption for the 1989 CBECS and published these estimates in 1993. This report presents PNNL previouslyCommercial end-use intensity (EUI), defined as energy consumption per square foot, will be used to 1) support the EIA commercial, sector energy modeling and forecasting efforts as part of the National Energy Modeling System (NEMS) and 2) augment the statistical summary information from the survey as published by the EIA. GENERAL APPROACHESDevelopment of EUIs for buildings can follow three general approaches: 1) direct metering, 2) statistical analysis known as Conditional Demand Analysis, and 3) engineering simulation. combination of the elements of the engineering simulation and Conditional Demand Analysis. model, begins by estimating end-use components with an engineering-oriented building simulation model. Predicted energy consumption for each end use in each CBECS sample building is dependent on some or all of the following factors : 1) bui 1 ding physical characteristics , 2) operating characteri sti cs, and 3) weather.The approach used in this study was a This approach, the Statistically Adjusted Engineering (SAE)The second stage of the SAE procedure uses the predicted end-use components as regressors to expl ai n actual total bui 1 ding energy consumption based on billing data. ment coefficients for each of the engineering-based end-use estimates.The regression model coefficients are interpreted as adjustThe V adjustment c o e f f i c i e n t s are then used t o generate t h e f i n a l end-use estimates for a l l b u i l d i n g s , i n c l u d i n g those t h a t may not have been i...
An example of the second type of inconsistency is shown by the following bills for premise number 2829400. The ending date for the first bill (3/17/2001) is consistent with the starting date for the second bill. However, there is no ending date for either the second or third bills and these bills have duplicate starting dates.An ending date for the second bill of 4/14/2005 can be derived from the number of days in the billing period (28). The missing ending date for the third bill can be filled as 5/20/2005, again consistent with the number of days and the starting date of the fourth bill. The adjusted series of bills, with the changed values in italics, then becomesThe public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY) SUBJECT TERMS
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