Estimating Residential Hot Water Use from Smart Electricity Data

In this project, we estimate residential hot water use from smart electricity data for areas in greater Chicago. We use a non-intrusive load monitoring (NILM) algorithm to estimate elctricty for water heating, as a measure of hot water consumption, using meter-level data for single-family residential homes (with electric space heat) at 30-minute resolution. Results indicate that water heating in the analyzed single-family residential homes accounted for 7-20% of total electricity consumption, representing an average of 40-60 gallons of hot water consumption per day. These results also demonstrated significant spatial variability, such that some areas of Chicago show higher per household hot water use. Our findings reveal that disaggregation of electricity meter data can provide an estimate of water heating; however, those estimates have significant uncertainty due to the temporal resolution of the electricity data. These findings 1) provide estimates (albeit overestimation) of hot water consumption in single-family residential homes; 2) demonstrate spatial variability in water heating and energy load; and 3) emphasize the need for finer temporal resolution electricity data for further study.

  • PI: Ashlynn S. Stillwell
  • PI Institution: University of Illinois
  • June 18, 2019 – December 31, 2020