Quantifying household energy performance using annual community baselines

Abstract

PurposeThis paper seeks to describe a protocol to estimate annual community energy consumption baselines for single‐family detached homes in the Gainesville Regional Utility service area of Alachua County, Florida, USA. Further, it details methods using these baselines to make direct comparisons of individual households' energy consumption and evaluate the energy impacts of three prescriptive demand side management (DSM) programs.Design/methodology/approachTo improve estimates of energy savings, the paper proposes using a “micro” scale multivariate regression methodology based on a census of utility and property appraiser household data.FindingsResults suggest that traditional analysis approaches are likely to overestimate savings significantly and that the annual community baseline technique provides more consistent estimates of energy savings than most commonly used methods.Practical implicationsThis type of analysis could provide a tool that utilities can use to more accurately and cost effectively measure DSM savings. This could result in reduced energy demand related to streamlined program setup and management.Originality/valueThe proposed methodology is unique in that it defines a new household‐level energy consumption baseline measure that we think is a more appropriate performance measure, uses a census of publicly available data for the population of interest, merging metered utility data with property appraiser data, and works upward to construct a simple model for evaluating household‐level energy consumption. The critical element that distinguishes our proposed energy performance measures is that they are calculated and interpreted using annual, population‐level, comparison‐group baselines that effectively normalize for community energy consumption patterns in any given year.

Housing Stock Characterization Study: An Innovative Approach to Measuring Retrofit Impact

“…To assess OUC residential energy efficiency retrofit impact, BEEHA used an evaluation protocol based on a near census 10 of utility and property appraiser household data for SFD homes in the OUC service territory (Jones et al 2010a). The principal goal of the ACB methodology is to calculate objectively normalized measures of EP, from which statistically and practically valid estimates of energy savings can be derived.…”

“…Jones et al (2010a) present a detailed explanation of the ACB method and an analysis that compares its results directly to those of other common methods of estimating energy savings (Schiller 2007), including time series, time series with normalized annual consumption, and time series with comparison groups. The analysis involved four main steps:…”

Housing Stock Characterization Study. An Innovative Approach to Measuring Retrofit Impact

“…This section explains technical details of the ACB approach used in this study. To assess OUC residential energy efficiency retrofit impact, BEEHA used an evaluation protocol based on a near census 10 of utility and property appraiser household data for SFD homes in the OUC service territory (Jones et al 2010a). The principal goal of the ACB methodology is to calculate objectively normalized measures of EP, from which statistically and practically valid estimates of energy savings can be derived.…”

Evaluating the Energy-Saving Effects of a Utility Demand-Side Management Program: A Difference-in-Difference Coarsened Exact Matching Approach

“…2 ) to engineering simulation models, 3 and to various econometric models combining monthly meter readings and available data on customer characteristics to determine energy savings (e.g. Jones et al, 2010;Cohen et al, 1991). Engineering methods use simulations to predict energy savings from specific measures at the individual building level or the end-use equipment level.…”