On performance evaluation and machine learning approaches in non-intrusive load monitoring

Abstract

Non-Intrusive Load Monitoring (NILM) is a set of techniques to gain deep insights into workflows inside buildings based on data provided by smart meters. In this way, the combined consumption needs only to be monitored at a single, central point in the household, providing advantages such as reduced costs for metering equipment. Over the years, a plethora of load monitoring algorithms has been proposed comprising approaches based on Hidden Markov Models (HMM), algorithms based on combinatorial optimisation, and more recently, approaches based on machine learning. However, reproducibility, comparability, and performance evaluation remain open research issues since there is no standardised way researchers evaluate their approaches and report performance. In this paper, the author points out open research issues of performance evaluation in NILM, presents a short survey of deep learning approaches for NILM, and formulates research questions related to open issues in NILM. An outline of future work is given including applied methodology and expected findings.

NILM-based approach for energy efficiency assessment of household appliances

“…Considering the load consumption disaggregation and based on the increasing energyawareness of individual equipment, consumers may adapt consumption behaviours, replace equipment or install management systems focusing on energy/money savings (Baets et al 2017;Mack et al 2019), either on residential, commercial or industrial scenarios (Sadeghianpourhamami et al 2017;Henriet et al 2018;Stankovic et al 2016). Recent NILM studies have been based on different attribute extraction methods, accuracy evaluations, and load disaggregation results ranging from 70% to 98% (Souza et al 2019;Sadeghianpourhamami et al 2017;Esa et al 2016;Wong et al 2013;Abubakar et al 2015;Le and Kim 2018;Aladesanmi and Folly 2015;Klemenjak 2018;Bao et al 2018;Gopinath et al 2020).…”