A Comprehensive Review and Outlook of Non-Intrusive Load Monitoring Systems

Non-Intrusive Load Monitoring (NILM) enables the disaggregation of aggregate electrical consumption in residential, commercial, and industrial settings to identify operational appliances and quantify their individual energy usage, eliminating the need for per-appliance metering. Recent years have witnessed transformative advances driven by the widespread adoption of machine learning, particularly deep learning algorithms, fundamentally reshaping methodologies for appliance classification and energy disaggregation. Concurrently, the proliferation of increasingly higher-frequency NILM datasets has been instrumental in developing sophisticated algorithms reliant on such data. This review paper synthesizes the most significant recent advancements in residential NILM research, elucidating the field’s trajectory. We propose a novel, comprehensive NILM framework structured into five core components: Data Acquisition , Event Detection , Feature Extraction , Appliance Classification , and Energy Disaggregation . This framework provides a more holistic analysis compared to prior models that overlooked critical details. Furthermore, the paper critically examines persistent challenges and limitations inherent in current NILM systems and proposes forward-looking solutions leveraging cloud computing, the Internet of Things (IoT), and mixed-signal controllers.