Investigating photovoltaic solar power output forecasting using machine learning algorithms
Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have estab...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2022.2126528 |
| _version_ | 1811205906386386944 |
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| author | Yusuf Essam Ali Najah Ahmed Rohaini Ramli Kwok-Wing Chau Muhammad Shazril Idris Ibrahim Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie |
| author_facet | Yusuf Essam Ali Najah Ahmed Rohaini Ramli Kwok-Wing Chau Muhammad Shazril Idris Ibrahim Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie |
| author_sort | Yusuf Essam |
| collection | DOAJ |
| description | Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting. |
| first_indexed | 2024-04-12T03:37:56Z |
| format | Article |
| id | doaj.art-b11c510545fb4843986099f8ec4c132c |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-04-12T03:37:56Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-b11c510545fb4843986099f8ec4c132c2022-12-22T03:49:21ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2022-12-011612002203410.1080/19942060.2022.2126528Investigating photovoltaic solar power output forecasting using machine learning algorithmsYusuf Essam0Ali Najah Ahmed1Rohaini Ramli2Kwok-Wing Chau3Muhammad Shazril Idris Ibrahim4Mohsen Sherif5Ahmed Sefelnasr6Ahmed El-Shafie7Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Selangor, MalaysiaInstitute of Energy Infrastructure (IEI), Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Selangor, MalaysiaDepartment of Informatics, College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, MalaysiaDepartment of Civil And Environmental Engineering, Hong Kong Polytechnic University, Kowloon, Hong KongDepartment of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Civil and Environmental Engineering, College of Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesNational Water and Energy Centre, United Arab Emirates University, Al Ain, United Arab EmiratesDepartment of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaSolar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting.https://www.tandfonline.com/doi/10.1080/19942060.2022.2126528Solar power forecastingdecision treerandom forestextreme gradient boostingartificial neural networklong short-term memory |
| spellingShingle | Yusuf Essam Ali Najah Ahmed Rohaini Ramli Kwok-Wing Chau Muhammad Shazril Idris Ibrahim Mohsen Sherif Ahmed Sefelnasr Ahmed El-Shafie Investigating photovoltaic solar power output forecasting using machine learning algorithms Engineering Applications of Computational Fluid Mechanics Solar power forecasting decision tree random forest extreme gradient boosting artificial neural network long short-term memory |
| title | Investigating photovoltaic solar power output forecasting using machine learning algorithms |
| title_full | Investigating photovoltaic solar power output forecasting using machine learning algorithms |
| title_fullStr | Investigating photovoltaic solar power output forecasting using machine learning algorithms |
| title_full_unstemmed | Investigating photovoltaic solar power output forecasting using machine learning algorithms |
| title_short | Investigating photovoltaic solar power output forecasting using machine learning algorithms |
| title_sort | investigating photovoltaic solar power output forecasting using machine learning algorithms |
| topic | Solar power forecasting decision tree random forest extreme gradient boosting artificial neural network long short-term memory |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2022.2126528 |
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