Renewable energy forecasting using advanced machine learning techniques

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search icon account icon shopping cart icon Toggle navigationTools Toggle navigationShare Home Books Sustainable Energy Systems: Planning, Integration and Management Chapter Renewable Energy Forecasting using Advanced Machine Learning Techniques image of Renewable Energy Forecasting using Advanced Machine Learning Techniques Authors: Himanshi1, Gaganpreet Kaur2, Jatin Arora3, Senthil Kumar AV4, Ujjwal Kaushik5, Chander Prabha6 View Affiliations Source: Sustainable Energy Systems: Planning, Integration and Management , pp 112-131 Publication Date: April 2026 Language: English Previous T o C Next Abstract Preview this chapter: The integration of renewable energy sources into the power grid presents significant challenges due to their recurrent and variable nature. Accurate forecasting of renewable energy generation is crucial for grid stability, efficient energy management, and the optimisation of energy resources. This chapter explores the application of advanced machine learning techniques in renewable energy forecasting, which focuses on solar and wind energy. It begins with an introduction to the importance of renewable energy forecasting and the role of machine learning in addressing forecasting challenges. The fundamentals of renewable energy forecasting are discussed, highlighting the key challenges and the necessity for accurate predictions. The chapter delves into various machine learning techniques, including regression, classification, clustering, deep learning, ensemble methods, and hybrid models. A comprehensive comparison between traditional and advanced machine learning techniques is provided. Hybrid models and ensemble methods are explored for their potential to enhance forecasting accuracy. Real-time forecasting and optimisation techniques are also covered, emphasising their applications in smart grids and microgrids. The chapter includes real-world case studies and applications, showcasing successful implementations of machine learning in renewable energy forecasting. The ethical and social implications of using AI and ML in this domain are discussed, along with policy and regulatory considerations. This chapter aims to provide a comprehensive overview of the state-of-the-art in renewable energy forecasting using advanced machine learning techniques, offering valuable insights for researchers, practitioners, and policymakers.

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Photo by Matthew Henry on Unsplash

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This chapter aims to provide a comprehensive overview of the state-of-the-art in renewable energy forecasting using advanced machine learning techniques, offering valuable insights for researchers, practitioners, and policymakers.

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