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Machine Learning and Meta-heuristic Algorithms for Renewable Energy: A Systematic Review

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Advanced Control and Optimization Paradigms for Wind Energy Systems

Part of the book series: Power Systems ((POWSYS))

Abstract

The demand for energy is become essential due to industrial activities and increasing agricultural of any nation. According to the aforementioned, the renewable energy resources available are very suitable to meet the ever-growing requirement of energy by the humanity rather causing any harmful effects to nature. Therefore, several research studies have been introduced in the renewable energy field such as solar, wind, biomass, and biogas due to the clean and sustainability. To better scheme and utilize this energy resource, good forecasting and optimization are necessary and intrinsic. So, this review introduces an overview of the renewable energy forecasting techniques that have been utilized in this field based on meta-heuristic optimization algorithms and machine learning (ML). In addition, several challenges have been addressed, recommendations for future research are provided, and a comprehensive bibliography is conducted. Eventually, in general speaking, this comprehensive review of renewable energy resources may help the researchers, energy planners, and policymakers.

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minya, Egypt

    Essam H. Houssein

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  1. Faculty of Automation and Computers, “Politehnica” University of Timișoara, Timișoara, Romania

    Radu-Emil Precup

  2. Department of Electrical and Electronics Engineering, Sakarya University, Serdivan, Sakarya, Turkey

    Tariq Kamal

  3. School of Electrical Engineering, Chongqing University, Chongqing, China

    Syed Zulqadar Hassan

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Houssein, E.H. (2019). Machine Learning and Meta-heuristic Algorithms for Renewable Energy: A Systematic Review. In: Precup, RE., Kamal, T., Zulqadar Hassan, S. (eds) Advanced Control and Optimization Paradigms for Wind Energy Systems. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-5995-8_7

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