Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques
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...
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2022
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1300 |
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| _version_ | 1857415062350725120 |
|---|---|
| author | Abu Zitar, Raed |
| author2 | Abualigah, Laith Almotairi, Khaled H. Hussein, Ahmad MohdAziz Abd Elaziz, Mohamed Nikoo, Mohammad Reza Gandomi, Amir H. |
| author2_role | author author author author author author |
| author_facet | Abu Zitar, Raed Abualigah, Laith Almotairi, Khaled H. Hussein, Ahmad MohdAziz Abd Elaziz, Mohamed Nikoo, Mohammad Reza Gandomi, Amir H. |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu Zitar, Raed Abualigah, Laith Almotairi, Khaled H. Hussein, Ahmad MohdAziz Abd Elaziz, Mohamed Nikoo, Mohammad Reza Gandomi, Amir H. |
| dc.date.none.fl_str_mv | 2022-08-23T07:48:45Z 2022-08-23T07:48:45Z 2022 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.3390/en15020578 https://depot.sorbonne.ae/handle/20.500.12458/1300 10.3390/en15020578 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Energies 1996-1073 |
| dc.subject.none.fl_str_mv | survey Artificial Intelligence (AI) optimization algorithm deep learning machine learning power generation storage systems renewable energy systems photovoltaic (PV) solar energy wind energy |
| dc.title.none.fl_str_mv | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | 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. |
| id | sorbonner_fafe00a11ae0a5e8c6503c7ac3b74ec9 |
| identifier_str_mv | 10.3390/en15020578 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1300 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning TechniquesAbu Zitar, RaedAbualigah, LaithAlmotairi, Khaled H.Hussein, Ahmad MohdAzizAbd Elaziz, MohamedNikoo, Mohammad RezaGandomi, Amir H.surveyArtificial Intelligence (AI)optimization algorithmdeep learningmachine learningpower generationstorage systemsrenewable energy systemsphotovoltaic (PV)solar energywind energyNowadays, 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.2022-08-23T07:48:45Z2022-08-23T07:48:45Z2022Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.3390/en15020578https://depot.sorbonne.ae/handle/20.500.12458/130010.3390/en15020578enEnergies1996-1073oai:depot.sorbonne.ae:20.500.12458/13002024-09-11T11:05:34Z |
| spellingShingle | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques Abu Zitar, Raed survey Artificial Intelligence (AI) optimization algorithm deep learning machine learning power generation storage systems renewable energy systems photovoltaic (PV) solar energy wind energy |
| title | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| title_full | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| title_fullStr | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| title_full_unstemmed | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| title_short | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| title_sort | Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques |
| topic | survey Artificial Intelligence (AI) optimization algorithm deep learning machine learning power generation storage systems renewable energy systems photovoltaic (PV) solar energy wind energy |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1300 |