Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning
Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the...
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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/10.1155/2022/1541938 |
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| author | K. Mukilan K. Thaiyalnayaki Yagya Dutta Dwivedi J. Samson Isaac Amarjeet Poonia Arvind Sharma Essam A. Al-Ammar Saikh Mohammad Wabaidur B. B. Subramanian Adane Kassa |
| author_facet | K. Mukilan K. Thaiyalnayaki Yagya Dutta Dwivedi J. Samson Isaac Amarjeet Poonia Arvind Sharma Essam A. Al-Ammar Saikh Mohammad Wabaidur B. B. Subramanian Adane Kassa |
| author_sort | K. Mukilan |
| collection | DOAJ |
| description | Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods. |
| format | Article |
| id | doaj-art-fa68d5043b1b4173a23fda66f07f4696 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-fa68d5043b1b4173a23fda66f07f46962025-02-03T05:53:35ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/1541938Prediction of Rooftop Photovoltaic Solar Potential Using Machine LearningK. Mukilan0K. Thaiyalnayaki1Yagya Dutta Dwivedi2J. Samson Isaac3Amarjeet Poonia4Arvind Sharma5Essam A. Al-Ammar6Saikh Mohammad Wabaidur7B. B. Subramanian8Adane Kassa9Department of Civil EngineeringSRM Institute of Science and TechnologyDepartment of Aeronautical EngineeringDepartment of Biomedical EngineeringDepartment of Information TechnologyDepartment of Electronics and Communication EngineeringDepartment of Electrical EngineeringChemistry DepartmentDepartment of BiotechnologyFaculty of Mechanical EngineeringSolar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.http://dx.doi.org/10.1155/2022/1541938 |
| spellingShingle | K. Mukilan K. Thaiyalnayaki Yagya Dutta Dwivedi J. Samson Isaac Amarjeet Poonia Arvind Sharma Essam A. Al-Ammar Saikh Mohammad Wabaidur B. B. Subramanian Adane Kassa Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning International Journal of Photoenergy |
| title | Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning |
| title_full | Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning |
| title_fullStr | Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning |
| title_full_unstemmed | Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning |
| title_short | Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning |
| title_sort | prediction of rooftop photovoltaic solar potential using machine learning |
| url | http://dx.doi.org/10.1155/2022/1541938 |
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