Machine Learning for Residential Energy Data Analytics Enhancing Energy Efficiency and Management: Datasets, Methods, and Applications

Abstract

Abstract Purpose of Review Residential energy analytics has advanced with increasing availability of energy data, e.g., from smart meters, to provide new insights into consumption behaviors and enhance energy efficiency and management. This review synthesizes public datasets, machine learning methods, and applications across two major analytical approaches: behavioral analysis through clustering and load disaggregation using Non-Intrusive Load Monitoring (NILM). We examine how characteristics of residential energy data are used in these two analytical approaches and analyze how data properties, including temporal resolution, population scale, and measurement granularity, relate to reported applications and performance. Recent Findings A structured analysis of 24 major residential energy datasets reveals distinct data utilization patterns across reported applications. Studies demonstrating consumption behavioral analysis through clustering typically utilize population scale datasets with medium temporal resolution for customer segmentation and demand reduction. In contrast, studies achieving effective load disaggregation through NILM often rely on high-frequency measurements for appliance-level disaggregation. These patterns reflect prevailing data collection and usage practices, while both analytical approaches continue to expand through integration of hybrid models and privacy-preserving training. Summary This review integrates methodological development, energy applications, and dataset utilization, providing a comprehensive understanding of residential energy analytics and future research directions. Continued progress is needed to address challenges related to data scarcity, methodological adaptability, and deployment constraints, including advances in privacy-preserving frameworks, such that residential energy efficiency and management can be further enhanced across diverse residential contexts.