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A Review of Machine Learning Models in Renewable Energy

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IoT for Sustainable Smart Cities and Society

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Abstract

Renewable energy is gradually being used to offset the impact of climate change and global warming. Various forecasting techniques have been introduced to enhance renewable energy prediction ability. The objective of this research is structured as follows: Firstly, this study examines machine learning methods for forecasting renewable energy resources. Secondly, this survey demonstrates the process implemented in the machine learning model for forecasting the performance of the machine learning model. Thirdly, the analyses of renewable energy forecasting models were conducted on the basis of mean absolute percentage error and correlation coefficient. Finally, at the conclusion of this study, several possible future work opportunities were identified.

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Authors and Affiliations

  1. Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India

    Anuj Gupta & Kapil Gupta

  2. Guru Jambheshwar University of Science and Technology, Hisar, India

    Sumit Saroha

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  1. Anuj Gupta
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  2. Kapil Gupta
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  3. Sumit Saroha
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  1. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China

    Joel J. P. C. Rodrigues

  2. Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India

    Parul Agarwal

  3. Campus Director, Delhi Skill and Entrepreneurship University, New Delhi, India

    Kavita Khanna

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Gupta, A., Gupta, K., Saroha, S. (2022). A Review of Machine Learning Models in Renewable Energy. In: Rodrigues, J.J.P.C., Agarwal, P., Khanna, K. (eds) IoT for Sustainable Smart Cities and Society. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-89554-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-89554-9_12

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