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 |
| Published: |
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 |
| Summary: | 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. |
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| ISSN: | 1994-2060 1997-003X |