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Abstract Residential water heating is among the most energy-intensive aspects of the water sector; however, residential hot water use is often poorly quantified. Estimating hot water consumption from smart electricity meter data can help advance the body of knowledge regarding the residential energy-water nexus by employing data to fill this knowledge gap, potentially promoting community resilience through energy and water resources efficiency. Using a non-intrusive load monitoring algorithm calibrated with fine-resolution data, we disaggregated electricity for water heating from half-hourly smart electricity meter data, demonstrated with data organized at the zip code level for areas in the city of Chicago. From these electricity for water heating signals, we estimated residential hot water consumption with quantified uncertainty. Results indicate that water heating accounted for 7%–20% of total electricity consumption in the analyzed single-family residential homes, representing an average of 1–8 kWh d −1 of electricity consumption and 7–55 gallons (26–208 l) of hot water per day. These results also demonstrated significant spatial variability, such that some areas of Chicago show higher per household hot water use. With the challenges of deploying advanced water metering infrastructure, using isolated water heating signals from smart electricity meters to develop a first-order estimate of domestic hot water use represents a valuable quantification of an energy-intense flow.
Bongungu et al. (Mon,) studied this question.