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

• Published in: Energies (Basel) Vol. 15; no. 2; p. 578
• Main Authors: Abualigah, Laith, Zitar, Raed Abu, Almotairi, Khaled H., Hussein, Ahmad MohdAziz, Abd Elaziz, Mohamed, Nikoo, Mohammad Reza, Gandomi, Amir H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2022; MDPI
• Subjects: Alternative energy sources; Artificial Intelligence; Computer applications; Computer Science; Deep learning; Electricity; Energy resources; Energy storage; Exports; Intelligence; Learning algorithms; Machine learning; Optimization; photovoltaic (PV); Photovoltaics; power generation; Power sources; Production methods; renewable energy systems; Renewable resources; Solar energy; storage systems; Taxonomy; Turbines; Velocity; wind energy; Wind power; deep learning; solar energy; Artificial Intelligence (AI); optimization; photovoltaic (PV); storage systems; survey; wind energy; renewable energy systems; power generation; machine learning; algorithm
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15020578

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.