Load control in smart grids: a review of NILM-, machine learn-, and reflectometry-based approaches - de Sousa Junior, João José; Meneguelo, Ana Paula; Custódio Coura, Daniel José; Cardoso Celeste, Wanderley (Latin American Journal of Energy Research, 2025)
Load control is essential for the efficient management of energy systems, especially in smart grids with distributed generation. This management relies on data collection through sensors, which are later processed by machine learning algorithms. This study aims to analyze the main load classification methods, the algorithms employed, and their various applications in real-world contexts. The adopted methodology consists of a comparative review of three approaches: (i) non-intrusive load monitoring (NILM), (ii) methods using sensors based on reflectometry, and (iii) independent techniques that utilize machine learning and the Internet of Things (IoT). The results indicate that, although NILM is widely used for load monitoring and independent methods offer diverse strategies for real-time tracking, approaches based on reflectometry sensors demonstrate greater potential in improving accuracy and enhancing load control. This study also highlights accessible and sustainable solutions for the energy sector and emphasizes the untapped potential of reflectometry in smart electrical grids, a recent and promising topic.