Brown bear optimized random forest model for short term solar power forecasting
Short term solar power forecasting is essential in managing the daily power requirements, electricity market operations and maintaining grid stability. Most of the ensemble ML algorithms outperform the traditional ML algorithms in terms of prediction accuracy. In this paper, short-term solar power f...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025006619 |
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| author | Rathika Senthil Kumar P.S. Meera V. Lavanya S. Hemamalini |
| author_facet | Rathika Senthil Kumar P.S. Meera V. Lavanya S. Hemamalini |
| author_sort | Rathika Senthil Kumar |
| collection | DOAJ |
| description | Short term solar power forecasting is essential in managing the daily power requirements, electricity market operations and maintaining grid stability. Most of the ensemble ML algorithms outperform the traditional ML algorithms in terms of prediction accuracy. In this paper, short-term solar power forecasting is done using random forest (RF) algorithm for a comparatively smaller data set. The results of the RF model are then compared with other ML models namely decision tree (DT), support vector regression (SVR), gradient boost (GB) and ridge regression (RR). The prediction accuracy of the RF model as assessed by MSE is increased by 27.63 %, R2 by 13.4 %, RMSE by 14.93 % and MAE by 19.17 % when compared with the DT model. To further improve the accuracy of the RF model, the hyperparameters of the random forest model are tuned using brown bear optimization algorithm (BBOA). Hyperparameter tuning using BBOA further improves MSE by 19.73 %, RMSE by 10.41 %, MAE by 11.19 % and R2 by 7.17 %. The results obtained are compared with hyperparameter tuning using particle swarm optimization (PSO) and firefly algorithm (FA). MSE obtained using BBOA is improved by 2.7 % and 3.7 % when compared with PSO and FA respectively. This improvement in the performance of BBOA can be attributed to the robust nature and better adaptation capability of the algorithm, proving its competence in hyperparameter tuning of ML model. |
| format | Article |
| id | doaj-art-9c2f690a12fe460a8035e415421b9e08 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-9c2f690a12fe460a8035e415421b9e082025-08-20T02:05:07ZengElsevierResults in Engineering2590-12302025-03-012510458310.1016/j.rineng.2025.104583Brown bear optimized random forest model for short term solar power forecastingRathika Senthil Kumar0P.S. Meera1V. Lavanya2S. Hemamalini3School of Electrical Engineering, Vellore Institute of Technology, Chennai, IndiaCorresponding author.; School of Electrical Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, Chennai, IndiaShort term solar power forecasting is essential in managing the daily power requirements, electricity market operations and maintaining grid stability. Most of the ensemble ML algorithms outperform the traditional ML algorithms in terms of prediction accuracy. In this paper, short-term solar power forecasting is done using random forest (RF) algorithm for a comparatively smaller data set. The results of the RF model are then compared with other ML models namely decision tree (DT), support vector regression (SVR), gradient boost (GB) and ridge regression (RR). The prediction accuracy of the RF model as assessed by MSE is increased by 27.63 %, R2 by 13.4 %, RMSE by 14.93 % and MAE by 19.17 % when compared with the DT model. To further improve the accuracy of the RF model, the hyperparameters of the random forest model are tuned using brown bear optimization algorithm (BBOA). Hyperparameter tuning using BBOA further improves MSE by 19.73 %, RMSE by 10.41 %, MAE by 11.19 % and R2 by 7.17 %. The results obtained are compared with hyperparameter tuning using particle swarm optimization (PSO) and firefly algorithm (FA). MSE obtained using BBOA is improved by 2.7 % and 3.7 % when compared with PSO and FA respectively. This improvement in the performance of BBOA can be attributed to the robust nature and better adaptation capability of the algorithm, proving its competence in hyperparameter tuning of ML model.http://www.sciencedirect.com/science/article/pii/S2590123025006619Solar power forecastingMachine learningHyperparameter tuningBrown bear optimizationRandom forest |
| spellingShingle | Rathika Senthil Kumar P.S. Meera V. Lavanya S. Hemamalini Brown bear optimized random forest model for short term solar power forecasting Results in Engineering Solar power forecasting Machine learning Hyperparameter tuning Brown bear optimization Random forest |
| title | Brown bear optimized random forest model for short term solar power forecasting |
| title_full | Brown bear optimized random forest model for short term solar power forecasting |
| title_fullStr | Brown bear optimized random forest model for short term solar power forecasting |
| title_full_unstemmed | Brown bear optimized random forest model for short term solar power forecasting |
| title_short | Brown bear optimized random forest model for short term solar power forecasting |
| title_sort | brown bear optimized random forest model for short term solar power forecasting |
| topic | Solar power forecasting Machine learning Hyperparameter tuning Brown bear optimization Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025006619 |
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