Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
It addresses the need for efficient demand-side management in residential energy systems, but is incremental as it reviews existing methods.
This paper reviews recent deep learning methods for Non-Intrusive Load Monitoring (NILM), which decomposes total household energy consumption into individual appliance profiles, and compares their accuracy using standard metrics and public databases.
Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.