LAUSR

Abstract With the active large-scale roll-out of smart metering worldwide, details about the type of smart meter data that will be available for analysis are emerging. Consequently, focus has steadily been shifting from analysis of high-rate power readings (usually in kHz to MHz) to low-rate power readings (sampled at 1–60 s) and very low-rate meter readings of the order of 15–60 min. This has triggered renewed research into practical non-intrusive load disaggregation of low- to very-low granularity meter readings to address challenges not addressed by existing disaggregation approaches, namely, indistinct appliance ON/OFF transitions, increased likelihood of overlapping appliance usage within a sample and noise due to unknown appliances. In this paper, focusing on smart meter readings at hourly resolution, three load disaggregation solutions are proposed based on: (i) optimisation (minimisation of error between aggregate and disaggregated loads), (ii) graph signal processing and (iii) convolutional neural network. These are benchmarked with state-of-the-art approaches, based on factorial hidden Markov model and combinatorial optimisation implemented in the NILMTK toolbox, and discriminative disaggregation sparse coding. The hourly electricity profile data is obtained from real-world active power readings from the REFIT dataset 1 over a period of longer than one year. All proposed disaggregation approaches outperform benchmarking methods for labelled appliances in terms of both energy performance metrics and faster execution time. The proposed approaches succeed in disaggregating, at very low resolutions, a wide range of loads including white goods even when there are unlabelled loads contributing to the meter readings.