A Comprehensive Review of Machine Learning Techniques for Smart Grid Optimization

In smart grid systems, machine learning (ML) improves energy network efficiency, reliability, and sustainability. This book chapter exhaustively covers how ML technologies are changing smart grid operations. It introduces smart grids and machine learning's role in optimizing them. The chapter then discusses smart grid machine learning algorithms such as supervised and unsupervised learning, reinforcement learning, and hybrid models. Implementation options for data gathering, preprocessing, model training, and system integration are discussed, emphasizing real-world deployment issues and solutions. The chapter examines successful case studies and real-world applications of ML in load forecasting, renewable energy integration, defect detection, and energy market optimization across worldwide contexts. Deep reinforcement learning, explainable AI, transfer learning, IoT integration, blockchain technology, and AI-driven cybersecurity are also discussed in the chapter. Examining policy and regulation to promote innovation and secure and successful ML implementation in smart grids is also important. This chapter concludes with realistic implementation tactics and research proposals to give researchers, practitioners, and policymakers a solid framework for smart grid optimization using machine learning. This evaluation helps drive the next generation of smart grid technology by linking present and future improvements.