A Scoping Review of Energy Load Disaggregation
It provides a comprehensive overview for researchers and practitioners in energy management, but it is incremental as it synthesizes existing literature without new methods or data.
This paper conducted a scoping review of energy load disaggregation by analyzing 72 journal articles, finding that domestic electricity consumption is the most researched area, with artificial neural networks being the most common method used.
Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper con-ducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.