Secondary Author(s)

Parker, Danny; Vieira, Robin; Fenaughty, Karen

Report Number




Utilities; Energy Conservation; Energy Analysis; Peak Demand


With the evolution of Advanced Metering Infrastructure (AMI) electric meters, utility companies now have direct access to whole building electricity use at a granular time scale. AMI data can be used for a variety of purposes beyond billing, for example, to evaluate the efficacy of energy conservation (EC) programs. Historical methods for calculating EC program savings include building simulation models and laboratory and/or field testing. With big data now available, which analysis methods are more likely to yield quality results?

In a recent project, the Orlando Utility Commission provided monitored AMI data from Oct 1, 2015 - Sep 27, 2018 for 2,832 Orange Country, Florida rebate participants. These participants had either already enrolled in a rebate program or had signed up to participate. The project objective was to analyze this large AMI data set to estimate the savings from a heat pump retrofit program in energy (kWh) and coincident peak demand (kW) relative to baseline efficiency levels.

This paper illustrates five methods of predicting EC program savings for a utility company's rebate program using: 1) side-by-side groups of current and future participants, 2) before and after evaluation of a stable group of participants, 3) evaluation of a before and after group using pooled regression, 4) regression on individual accounts with results then averaged, and 5) building simulation model results are used as a comparative baseline. This effort was pursued to see if the bias between the various participant segments could be reduced to focus on energy differences within the retrofit equipment itself.

This paper was published in the 2020 ACEEE Summer Study on Energy Efficiency in Buildings.

Date Published



Buildings - Energy Analysis; Buildings - Energy Conservation; Buildings - Peak Demand; Utilities



Rights Statement

In Copyright