A Proposed Method for Improving Residential Heating Energy Estimates Based on Billing Data

Abstract

Analyses o f residential energy conservation programs frequently require reliable estimates of program effects on space-heating energy consumption. Simulation models are often used to provide such estimates. Recent, large-scale programs to collect empirical energy consumption data, however, provide a basis for alternative ways to estimate program effects that utilize the empirical data. The PRISM methodology uses relatively inexpensive billing and weather data to estimate base and temperature-sensitive (primarily space-heating) loads. We used billing data from over 300 manufactured (mobile) homes in two residential conservation projects to derive PRISM heating energy estimates. Actual heating energy data for a subset of these homes was used to develop a methodology for adjusting the initial PRISM-based heating estimates. We developed the adjustment relying on a theoretical approach and the empirical data. This approach resulted in a correction technique that reduced the average error in the initial PRISM-based space-heating estimates by about 70%. and requires primarily readily available PRISM outputs and limited housing characteristics data.