@article{24973,
  keywords = {End-use, Energy End-Use Forecasting, EUF},
  author = {Francis X Johnson and James W Hanford and Richard E Brown and Alan H Sanstad and Jonathan G Koomey},
  title = {Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1},
  abstract = {<p>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.</p>},
  year = {1994},
  month = {06/1994},
  publisher = {Lawrence Berkeley Laboratory},
  isbn = {LBL-34045, UC-1600},
  language = {eng},
}