A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre data. Issue 2 (4th March 2022) - Ham, Sang woo; Karava, Panagiota; Bilionis, Ilias; Braun, James
Abstract : We present a scalable and practical method for disaggregating electrical usage for heat pump heating and cooling (HC) that uses low-resolution data from existing smart energy metres and smart thermostats. The disaggregation model is based on a Bayesian approach to account for the skewed characteristics of HC and non-HC energy consumption and adopts sequential Bayesian update to enable reliable predictions without long-term data. The modelling approach is demonstrated using disaggregated electricity consumption and thermostat operation signal data in two multi-family residential communities located in two different cities in Indiana, U.S. The results show that the model successfully disaggregated HC electricity consumption for various housing units by using 15-minute interval data with less than 12% error for a weekly time interval. Finally, seasonal parameters of the model were updated when a new HC operation signal was observed resulting in good predictions for different seasons.