Non-intrusive load disaggregation solutions for very low-rate smart meter data

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 to 60 sec) and very low-rate meter readings of the order of 15-60 minutes. 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

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