An Optimal Algorithm for Renewable Energy Generation Based on Neural Network
Solar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in man...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/8072269 |
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| _version_ | 1850157421206437888 |
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| author | Weihua Zhao Imran Khan Shelily F. Akhtar Mujahed Al-Dhaifallah |
| author_facet | Weihua Zhao Imran Khan Shelily F. Akhtar Mujahed Al-Dhaifallah |
| author_sort | Weihua Zhao |
| collection | DOAJ |
| description | Solar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in many nations across the world. The power sequence of PV is influenced by a variety of diverse variables, and it is very unpredictable and volatile. Unlike the distributed PVs, the centralized PVs have the same intensity and location. The obstruction of clouds causes minor variations in the output power of the PV, making the power forecasting more difficult. To solve the aforementioned difficulties, this article provides a new neural network-based technique for PV power optimization and forecasting. The first stage is to create a cloud trajectory tracking system based on cloud photos taken from the ground. Second, a cloud trajectory tracking-based irradiance coefficient prediction model was built. Then, to increase forecast accuracy, build an error correcting model. For verification, data from a centralized solar power station was used. The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy. |
| format | Article |
| id | doaj-art-39c9fdf6449149cca40c2ff6fb4632bd |
| institution | OA Journals |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-39c9fdf6449149cca40c2ff6fb4632bd2025-08-20T02:24:08ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/8072269An Optimal Algorithm for Renewable Energy Generation Based on Neural NetworkWeihua Zhao0Imran Khan1Shelily F. Akhtar2Mujahed Al-Dhaifallah3State Grid Luoyang Electric Power Supply CompanyDepartment of Electrical EngineeringElectrical Engineering DepartmentControl & Instrumentation Engineering DepartmentSolar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in many nations across the world. The power sequence of PV is influenced by a variety of diverse variables, and it is very unpredictable and volatile. Unlike the distributed PVs, the centralized PVs have the same intensity and location. The obstruction of clouds causes minor variations in the output power of the PV, making the power forecasting more difficult. To solve the aforementioned difficulties, this article provides a new neural network-based technique for PV power optimization and forecasting. The first stage is to create a cloud trajectory tracking system based on cloud photos taken from the ground. Second, a cloud trajectory tracking-based irradiance coefficient prediction model was built. Then, to increase forecast accuracy, build an error correcting model. For verification, data from a centralized solar power station was used. The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy.http://dx.doi.org/10.1155/2022/8072269 |
| spellingShingle | Weihua Zhao Imran Khan Shelily F. Akhtar Mujahed Al-Dhaifallah An Optimal Algorithm for Renewable Energy Generation Based on Neural Network International Journal of Photoenergy |
| title | An Optimal Algorithm for Renewable Energy Generation Based on Neural Network |
| title_full | An Optimal Algorithm for Renewable Energy Generation Based on Neural Network |
| title_fullStr | An Optimal Algorithm for Renewable Energy Generation Based on Neural Network |
| title_full_unstemmed | An Optimal Algorithm for Renewable Energy Generation Based on Neural Network |
| title_short | An Optimal Algorithm for Renewable Energy Generation Based on Neural Network |
| title_sort | optimal algorithm for renewable energy generation based on neural network |
| url | http://dx.doi.org/10.1155/2022/8072269 |
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