Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms.
Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for dist...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315955 |
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| author | Huu Nam Nguyen Quoc Thanh Tran Canh Tung Ngo Duc Dam Nguyen Van Quan Tran |
| author_facet | Huu Nam Nguyen Quoc Thanh Tran Canh Tung Ngo Duc Dam Nguyen Van Quan Tran |
| author_sort | Huu Nam Nguyen |
| collection | DOAJ |
| description | Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045 samples, factors like Humidity, Ambient temperature, Wind speed, Visibility, Cloud ceiling and Pressure serve as inputs for constructing these machine learning models in forecasting solar energy. Model accuracy is meticulously assessed and juxtaposed using metrics such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. SHAP analysis reveal that ambient temperature and humidity have the greatest influences on the value solar energy generated from photovoltaic panel. |
| format | Article |
| id | doaj-art-20d158c34d764df283dab4e65f49250b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-20d158c34d764df283dab4e65f49250b2025-01-08T05:31:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031595510.1371/journal.pone.0315955Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms.Huu Nam NguyenQuoc Thanh TranCanh Tung NgoDuc Dam NguyenVan Quan TranSolar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045 samples, factors like Humidity, Ambient temperature, Wind speed, Visibility, Cloud ceiling and Pressure serve as inputs for constructing these machine learning models in forecasting solar energy. Model accuracy is meticulously assessed and juxtaposed using metrics such as coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. SHAP analysis reveal that ambient temperature and humidity have the greatest influences on the value solar energy generated from photovoltaic panel.https://doi.org/10.1371/journal.pone.0315955 |
| spellingShingle | Huu Nam Nguyen Quoc Thanh Tran Canh Tung Ngo Duc Dam Nguyen Van Quan Tran Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. PLoS ONE |
| title | Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. |
| title_full | Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. |
| title_fullStr | Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. |
| title_full_unstemmed | Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. |
| title_short | Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. |
| title_sort | solar energy prediction through machine learning models a comparative analysis of regressor algorithms |
| url | https://doi.org/10.1371/journal.pone.0315955 |
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