Machine Learning based Predictive Modelling and Control for Load-Aware Energy Systems

Abstract

The global energy sector currently confronts mounting difficulties. Rising energy demands, enhanced efficiency requirements, and evolving patterns of supply and demand—coupled with insufficient management analyses—are placing significant pressure on the industry, especially within developing nations. Leveraging machine learning (ML) technologies to analyze energy data offers a promising strategy to overcome these obstacles. ML algorithms possess the capacity to scrutinize equipment performance, develop robust predictive models, and address sustainability challenges. For instance, in smart city environments, these algorithms autonomously adjust to fluctuations in electricity pricing and efficiently regulate energy consumption. Moreover, ML-driven systems assist energy providers by facilitating adaptation to the intermittent nature of renewable energy sources, thereby stabilizing grid performance. As global interest in low-emission energy solutions intensifies and reliance on oil diminishes, the deployment of renewable energy systems—such as solar photovoltaic arrays, wind turbines, and marine energy technologies—has experienced unprecedented growth worldwide. Consequently, artificial intelligence and machine learning have emerged as indispensable tools for effectively managing the multifaceted challenges facing the energy sector. In addition, controlling microgrids poses considerable difficulties that necessitate the use of sophisticated methods like model predictive control (MPC). This research article is dedicated to examining energy management within microgrids through the application of MPC, while also offering a comprehensive review of the most recent advancements in MPC strategies geared towards promoting sustainable energy management.