Improving device-level electricity consumption breakdowns in private households using ON/OFF events

Abstract

Smart meters can measure the electricity consumption of a household at a fine temporal granularity. By adequately processing this aggregated data an estimation of the consumption of individual appliances can be retrieved and used to provide novel services, such as personalized recommendations on how to reduce the overall energy consumption of the household. In this paper, we build upon existing work in consumption data disaggregation and consider smart meter data along with additional information made available by networked sensors and household appliances. In particular, we investigate the use of ON/OFF events, which signal when appliances have been turned on or off, to improve the accuracy of a state-of-the art disaggregation algorithm that uses such events along with smart meter data to estimate the consumption of single appliances. Our results, obtained by applying the algorithm to a publicly available dataset, show that the accuracy of the algorithm quickly deteriorates as the number of available ON/OFF events decreases. We thus suggest possible countermeasures to cope with this limitation and to provide accurate electricity consumption breakdowns in private households.