A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings

Abstract

Non-intrusive appliance load monitoring has emerged as an attractive approach to study energy consumption patterns without instrumenting every device in a building. The ensuing computational problem is to disaggregate total energy usage into usage by specific devices, to gain insight into consumption patterns. We exploit the temporal ordering implicit in on/off events of devices to uncover motifs (episodes) corresponding to the operation of individual devices. Extracted motifs are then subjected to a sequence of constraint checks to ensure that the resulting episodes are interpretable. Our results reveal that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices. With suitably configured processing steps, we demonstrate the applicability of our method to both residential and commercial buildings.