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Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Author

Listed:
  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Sciences, Universiti Sains Malaysia, George Town 11800, Pulau Pinang, Malaysia)

  • Raed Abu Zitar

    (Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi 38044, United Arab Emirates)

  • Khaled H. Almotairi

    (Computer Engineering Department, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Ahmad MohdAziz Hussein

    (Deanship of E-Learning and Distance Education, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Artificial Intelligence Science and Engineering, Galala University, Suze 435611, Egypt
    School of Computer Science and Robotics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Mohammad Reza Nikoo

    (Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat 123, Oman)

  • Amir H. Gandomi

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

Suggested Citation

  • Laith Abualigah & Raed Abu Zitar & Khaled H. Almotairi & Ahmad MohdAziz Hussein & Mohamed Abd Elaziz & Mohammad Reza Nikoo & Amir H. Gandomi, 2022. "Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques," Energies, MDPI, vol. 15(2), pages 1-26, January.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:578-:d:724453
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    Citations

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    Cited by:

    1. Sun, Jingbo & Wang, Yang & He, Yuan & Cui, Wenrui & Chao, Qingchen & Shan, Baoguo & Wang, Zheng & Yang, Xiaofan, 2024. "The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China," Applied Energy, Elsevier, vol. 360(C).
    2. Zhang, Zhonglian & Yang, Xiaohui & Yang, Li & Wang, Zhaojun & Huang, Zezhong & Wang, Xiaopeng & Mei, Linghao, 2023. "Optimal configuration of double carbon energy system considering climate change," Energy, Elsevier, vol. 283(C).
    3. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    4. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.

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