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Applications of Machine Learning for Renewable Energy: Issues, Challenges, and Future Directions

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Handbook of Smart Energy Systems

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Abstract

The world is facing a number of challenges related to energy sustainability, and the energy demand from cleaner sources of energy is increasing. If the demand is not addressed properly, this will lead to economic instability. The use of renewable energy resources is growing rapidly. There is a huge amount of energy loss in the energy sector. These losses add pressure on the energy industry. Demand being higher than supply of power, destabilizes the power grid and causes power quality degradation whereas lower power demand than the supply of power causes economic loss and energy wastage. In order to maintain power stability, research is focused on energy supply and demand to predict the amount of energy required. To meet the challenges of forecasting the energy available, machine learning methods are widely used to revolutionize the way we deal with renewable energy. This chapter explores the applications of machine learning in renewable energy especially solar and wind energy and addresses the issues related to renewable energy generation.

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  1. Vardhaman College of Engineering, Hyderabad, India

    M. A. Jabbar & Syed Saba Raoof

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  1. M. A. Jabbar
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  2. Syed Saba Raoof
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Editors and Affiliations

  1. Information Technology and Decision Sciences, University of North Texas, Denton, TX, USA

    Mahdi Fathi

  2. Department of Energy, Politecnico di Milano, MILANO, Milano, Italy

    Enrico Zio

  3. Industrial & Systems Engineering, University of Florida, GAINESVILLE, FL, USA

    Panos M. Pardalos

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Jabbar, M.A., Raoof, S.S. (2021). Applications of Machine Learning for Renewable Energy: Issues, Challenges, and Future Directions. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_71-1

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