Estimating Residential Hot Water Use with Smart Electricity Data


Project Aims:

In this project, we aim to estimate residential hot water use from smart electricity data for areas in greater Chicago. We estimate electricity 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.


We hypothesize that “smart” electricity meter data can provide an estimate of hot water consumption, via electricity for water heating.


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; 

3) emphasize the need for finer temporal resolution electricity data for further study.


We use non-intrusive load monitoring (NILM) to reveal the electricity signal for estimated water heating “on” events. Given the uncertainty in NILM techniques and the lack of ground-truthed water heater data, we conducted the disaggregation over a selected 2-week window with daily load patterns and weather conditions reflecting a likely lack of heating or air conditioning usage.


The results of our work are currently in preparation for publication in a journal manuscript and Joseph Bongungu’s M.S. thesis. Both publications will be submitted in May/June 2020. Results indicate that water heating in the analyzed single-family residential homes accounted for 8-17% of total electricity consumption, representing an average of 40-60 gallons of hot water consumption per day.

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