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

Laith Abualigah, Raed Abu Zitar, Khaled H. Almotairi, Ahmad MohdAziz Hussein, Mohamed Abd Elaziz, Mohammad Reza Nikoo and Amir H. Gandomi
Additional contact information
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
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
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

Energies, 2022, vol. 15, issue 2, 1-26

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.

Keywords: wind energy; solar energy; photovoltaic (PV); renewable energy systems; storage systems; power generation; machine learning; deep learning; optimization; algorithm; Artificial Intelligence (AI); survey (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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