A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre data. Issue 2 (4th March 2022)

• Record Type: Journal Article
• Authors: Ham, Sang woo; Karava, Panagiota; Bilionis, Ilias; Braun, James
• Is Part Of: Journal of building performance simulation. Volume 15:Issue 2(2022)
• Page Start: 251
• Page End: 267
• Publication Date: 2022-03-04
• Subjects: Energy disaggregation -- residential heating and cooling energy -- nonintrusive load monitoring -- NILM -- smart metre -- smart thermostat; 690.0113
• DOI: 10.1080/19401493.2022.2032352
• Languages: English
• ISSNs: 1940-1493

Abstract

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.