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

Koomey, Jonathan; Brown, Richard E.; Richey, R.C.

doi:10.2172/451200

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Detailed and geographically explicit bottom-up models (e.g. Koomey et al, 1995;MacDonald and Livengood, 2000) include housing stocks with typical characteristics like floor space, but also insulation, retrofit-options and heating equipment. More aggregate models (e.g.…”

Section: Floor Spacementioning

confidence: 99%

Model projections for household energy use in India

et al. 2011

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Energy use in developing countries is heterogeneous across households. Present day global energy models are mostly too aggregate to account for this heterogeneity. Here, a bottom-up model for residential energy use that starts from key dynamic concepts on energy use in developing countries is presented and applied to India. Energy use and fuel choice is determined for five end-use functions (cooking, water heating, space heating, lighting and appliances) and for five different income quintiles in rural and urban areas. The paper specifically explores the consequences of different assumptions for income distribution and rural electrification on residential sector energy use and CO 2 emissions, finding that results are clearly sensitive to variations in these parameters. As a result of population and economic growth, total Indian residential energy use is expected to increase by around 65-75% in 2050 compared to 2005, but residential carbon emissions may increase by up to 9-10 times the 2005 level. While a more equal income distribution and rural electrification enhance the transition to commercial fuels and reduce poverty, there is a trade-off in terms of higher CO 2 emissions via increased electricity use.

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Section: Floor Spacementioning

confidence: 99%

Model projections for household energy use in India

et al. 2011

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“…Both the complexity and the sheer magnitude of energy consumption in this end-use suggest that space conditioning must be ascribed an important role in any residential sector policies or programs aimed at improving energy efficiency and/or reducing energy consumption. The baseline forecast using the REEPS model described here is presented in a separate report (Koomey et al 1994a).…”

Section: Disclaimermentioning

confidence: 99%

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

Johnson¹,

Brown²,

Hanford³

et al. 1994

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This report details the data, assumptions and methodology for end-use forecasting of space conditioning energy use in the U.S. residential sector. Space conditioning end-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 by the Electric Power Research Institute (McMenamin et al. 1992). This modeling framework treats space conditioning separately from appliances such as refrigerators or water heating due to the complex physical and economic interactions that characterize HVAC systems, and because space conditioning is the most significant end-use of residential energy in the United States. Space conditioning accounts for approximately 30% of electricity consumption, 70% of natural gas consumption and 90% of oil consumption in the U.S. residential sector. In terms of primary energy, space conditioning represents over half of all energy consumption in residences (EIA 1993). This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline residential space conditioning end-use models. Analysis steps documented in this report include: defining the thermal shell characteristics, gathering technology and market data for HVAC equipment and systems, developing cost data for the various components of the thermal shell and HVAC systems, and specifying decision models (both the functional form and equation parameters) 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 efficiency standards. The resulting residential HVAC forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national and north/south levels. FOREWORD AND ACKNOWLEDGMENTS This project was undertaken at Lawrence Berkeley Laboratory (LBL) under the direct supervision of Jonathan Koomey, and with the guidance of James E. McMahon and Mark D. Levine. The purpose of this work is to create a well-documented end-use forecasting model for the residential sector, and to use that model to analyze national policies affecting the efficiency and patterns of energy use in the U.S. The work adopts the modeling framework of the Residential End-Use Energy Planning System (REEPS) developed for the Electric Power Research Institute (EPRI). This report is one of a series of three reports documenting the residential sector end-use forecasting project using EPRI-REEPS 2.1, which is the most recent version of the computer software. The first report in the series summarizes the model framework and inputs, and presents the results of the baseline forecast for each end-use (LBL-34044). The other two reports detail the data, assumptions and methodology for the two distinct groups of end-uses in the residential sector. This report (LBL-34045), the second in the se...

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“…Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. The baseline forecast using the REEPS model described here is presented in a separate report (Koomey et al 1994a).…”

Section: Introductionmentioning

confidence: 99%

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

Johnson¹

1994

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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 def'med 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 _esidential 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 _Jaroughthe 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 (both the functional form and equation parameters) 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.The proliferation of models and model parameters and the lack of detailed documentation in the end-use forecasting area have been sources of considerable confusion for practitioners and policy-makers alike. By making explicit the data and assumptions behind our analysis, we seek to clear up some of this confusion and move the energy efficiency debate beyond simply the numerical results of models.

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

Cited by 7 publications

References 3 publications

Model projections for household energy use in India

Model projections for household energy use in India

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

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

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