Advanced Machine Learning in Smart Grids: An overview

Abstract

Adopting Advanced Machine Learning for Smart Grids (ML-SG) is a promising strategy that revolutionizes the energy industry to optimize energy usage, improve grid management, and foster sustainability. It also increases the efficiency, reliability, and sustainability of contemporary power systems. Furthermore, incorporating machine learning into smart grids has important practical ramifications and can help address some of the most pressing issues facing contemporary energy systems. By precisely forecasting consumption trends and facilitating dynamic pricing models that take into account current grid circumstances, Machine Learning (ML) can improve demand response tactics. Additionally, it is essential for preserving grid stability since it can promptly identify irregularities and react to system oscillations, preventing blackouts and equipment failures. Furthermore, through supply and demand balance, energy dispatch optimization, and solar and wind power forecasts, ML makes it easier to seamlessly integrate renewable energy sources. These characteristics facilitate the shift to a more robust, adaptable, and ecologically friendly energy infrastructure in addition to increasing operating efficiency. In this paper, we investigate the development of ML solutions that benefit from the enormous amounts of data generated by IoT devices in the smart grid. Furthermore, this study examines the benefits and drawbacks of the adoption of ML-SG and offers an outline of their use while highlighting the implications of integrating ML into smart grids. In addition, it explores and analyzes how ML algorithms can be used for load forecasting and enabling accurate and real-time decision making in smart grids. The objective of this work is to analyze smart grid operations at different levels, such as predicting energy demand, identifying abnormalities, and reducing cybersecurity threats by using sophisticated ML-based algorithms, especially discussing attacks and countermeasures against these ML models. This work concludes with suggestions and recommendations that highlight the importance of improving the security and accuracy of ML-SG, while shedding some light on future directions. In the future, this work aims to contribute to the development of efficient ML solutions for energy infrastructure to become more effective and sustainable, by discussing data science and ML issues related to smart grids.