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

In: Handbook of Smart Energy Systems

Author

Listed:
  • M. A. Jabbar

    (Vardhaman College of Engineering)

  • Syed Saba Raoof

    (Vardhaman College of Engineering)

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.

Suggested Citation

  • M. A. Jabbar & Syed Saba Raoof, 2023. "Applications of Machine Learning for Renewable Energy: Issues, Challenges, and Future Directions," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 735-747, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_71
    DOI: 10.1007/978-3-030-97940-9_71
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