A comprehensive review of machine learning and deep learning models for non-intrusive load monitoring: performance, analyses, practical insights, and emerging trends
Muhammad Hammad Saleem, Muhammad Taha, Muhammad Asif Ali Rehmani, Shafiqur Rahman Tito, Snjezana Soltic, Pieter Nieuwoudt, Neel Pandey, Mollah Daud Ahmed
Abstract
Abstract Non-intrusive load monitoring (NILM) is widely regarded as a key method for energy disaggregation to estimate the energy consumption and operating time of individual equipment and appliances. Over the past few years, NILM has achieved substantially high accuracy through the incorporation of several machine learning (ML) and deep learning (DL) algorithms. This article reviews ML/DL-based approaches that have been applied to address NILM-related problems. This review focuses on five research questions: First, which problems associated with NILM systems have been addressed in recent studies using ML and DL models, and how well have the ML and DL models addressed NILM-related problems? Second, which ML and DL models have been the most explored for NILM, and how do the ML and DL algorithms perform on different NILM datasets? Third, how has model interpretability been addressed in ML/DL-based NILM methods, and what techniques have been proposed to improve the transparency of predictions? Fourth, what are the practical requirements of ML-based NILM systems in real-world settings? Finally, what are the main challenges of ML-based NILM systems in terms of datasets, models, and their implementation? This review also provides recommendations for addressing the identified research gaps and emerging trends and technologies in NILM and presents directions for future research.