Electricity Usage Proﬁle Disaggregation of Hourly Smart Meter Data Bochao Zhao, Lina Stankovic, and Vladimir Stankovic Department of Electronic and Electrical Engineering University of Strathclyde, Glasgow, G1 1XW, UK Email: {b.zhao, lina.stankovic, vladimir.stankovic}@strath.ac.uk Abstract—This paper is motivated by the growing demand of disaggregating electricity consumption measured by smart meters, down to appliance level. The very low 15-min to 60- min granularity of energy measurements available for analysis, as is standard by the majority of nationwide smart metering programmes, is posing serious challenges. The non-intrusive load monitoring (NILM) solutions for these very low data rates cannot leverage on low (1-60sec) to high rates (in the order of kHz to MHz) NILM approaches, and so far have not received much attention in the literature. In this paper, we propose a novel electricity proﬁle hourly disaggregation of energy consumed (kWh) based on K-nearest neighbours (K-NN), that relies on features such as statistical measures of the energy signal, time usage proﬁle of appliances and reactive power consumption (if available). We propose relative standard deviation as a metric to assess the quality of each feature per appliance. For validation, three publicly accessible real-world datasets are used, namely the REDD, REFIT and AMPds (Version 2), for up to 3 months. I. INTRODUCTION Non-intrusive load monitoring (NILM) is deﬁned as esti- mating individual appliance energy usage from smart meter readings (e.g., voltage, current, power) of a whole household using purely software tools [1]. Though various machine learning methods including Hidden Markov Model (HMM) and its variants, Dynamic Time Warping (DTW) [2], K-nearest neighbours (K-NN) [3]-[5], sparse coding [6], Neural Network [7] and Graph Signal Processing [8] have been applied to the NILM problem, most approaches focus on either high sampling rates in the order of kHz or MHz or sampling rates between 1sec and 1min. Disaggregation in these cases is usually performed via feature extraction and state transition modelling on active and/or reactive power data. Very low-rate (10-60 mins) NILM is slowly gaining interest [3]-[6], [9], [10] since electricity meters deployed at scale in most countries tend to provide extremely low-rate measurements, at 15, 30 minutes or hourly granularity. Compared with power measure- ments of higher granularity, the energy consumption signal at very low granularity features limited state transitions, fewer low-consuming appliances’ feature patterns and much higher probability of multiple appliances running simultaneously. Thus, lack of well-known features and increased appliance noise make very low NILM a challenging problem [4], which can be looked at as electricity usage proﬁle disaggregation, since the input is the total energy use within ﬁxed time intervals (e.g., in kiloWatt-hour), instead of active/reactive power readings collected at relatively high frequency in Watts or VArs. Sparse coding is proposed for hourly measurements in [6], where appliance models built from weekly typical training sets are used to predict appliance-level power consumption for unseen houses. Plug-level disaggregation results for hourly power consumption data is shown in [6] but the aggregate power consumption used is artiﬁcially obtained as the sum of sub-metered components disregarding measurement noise and noise due to unknown appliances, typically present in aggregate smart meter data. In [9], piecewise functions of power consumption versus external temperature per house are modelled for hourly disaggregation of base-load, heating and cooling consumption but not speciﬁc to any appliances. Sim- ilarly, appliance-level disaggregation results are not demon- strated in [10], where contextual supervision is applied to the single-channel source separation problem for overall heating, cooling and base load. K-NN, as a low-complexity time-series classiﬁcation/clus