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Machine Learning Applications for Renewable Energy Systems

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Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

The world is relying more and more on renewable energy sources to cater the global energy demand. Consequently, the renewable energy systems are becoming more and more intricate and therefore require state-of-the-art machine learning methodologies to operate and manage. This chapter presents a detailed overview of some key applications of artificial intelligence (AI) and machine learning (ML) for renewable energy with a particular focus on the challenges, available resources, and potential future research opportunities. In detail, the chapter discusses AI and ML applications in weather forecasting, power production, energy consumption forecasting, smart grids, and prognostic maintenance of renewable energy systems. An overview of the most commonly used AI and ML algorithms in the domain along with a detailed description of some of the publicly available datasets for training and evaluation of these algorithms to carry out different tasks in the renewable energy sector is also provided.

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Authors and Affiliations

  1. Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Yasir Saleem Afridi & Laiq Hassan

  2. Department of Computer Science, Munster Technological University, Cork, Ireland

    Kashif Ahmad

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Correspondence to Yasir Saleem Afridi .

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  1. Department of Mathematics, Punjabi University Patiala, Patiala, Punjab, India

    Mukhdeep Singh Manshahia

  2. Federal Scientific Agroengineering Center VIM, Moscow, Russia

    Valeriy Kharchenko

  3. Institute of Applied Mathematics, Middle East Technical University, Ankara, Türkiye

    Gerhard-Wilhelm Weber

  4. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pandian Vasant

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Afridi, Y.S., Hassan, L., Ahmad, K. (2023). Machine Learning Applications for Renewable Energy Systems. In: Manshahia, M.S., Kharchenko, V., Weber, GW., Vasant, P. (eds) Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26496-2_5

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