Machine-Learning-Based Smart Energy Management Systems: A Review

This work delves into the significant impact of Machine Learning (ML) on the advancement and improvement of Energy Management Systems (EMS), focusing on the incorporation of renewable energy sources, smart grids, and the general enhancement of energy efficiency, reliability, and sustainability. The main aim of this work is to offer a detailed summary of Machine Learning technologies that can be used in modern energy systems. It explains how these technologies can improve certain tasks like load forecasting, energy optimization, predictive maintenance, fault detection and diagnosis, and incorporating renewable energy systems supported by relevant approaches and application areas. Moreover, the work examines the benefits and opportunities presented by machine learning in boosting efficiency, enhancing system stability and resilience, and contributing to environmental sustainability. In addition, it identifies challenges and outlines future research needed to facilitate the adoption of ML in energy systems. In conclusion, the study underlines the critical role of machine learning in the evolution of energy systems and underscores the importance of collaborative efforts to overcome existing challenges and fully leverage machine learning's potential in the smart energy management systems domain.