Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques
Hydropower plays a crucial role in electricity generation, contributing over 60% of total renewable energy output. Its ability to stabilize energy fluctuations makes it essential in green energy initiatives. Accurate prediction of hydropower production is vital, considering its dependence on various...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005872 |
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| author | Zhenya Qi Yudong Feng Shoufeng Wang Chao Li |
| author_facet | Zhenya Qi Yudong Feng Shoufeng Wang Chao Li |
| author_sort | Zhenya Qi |
| collection | DOAJ |
| description | Hydropower plays a crucial role in electricity generation, contributing over 60% of total renewable energy output. Its ability to stabilize energy fluctuations makes it essential in green energy initiatives. Accurate prediction of hydropower production is vital, considering its dependence on various factors like weather, water storage, and electricity generation. Traditional methods struggle with the complexities involved. This study utilized Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) algorithms, both individually and in hybrid models enhanced by optimization techniques like Slime Mould Algorithm (SMA), Aquila Optimizer (AO), and Grey Wolf Optimization (GWO). XGBoost outperformed SVR in single model predictions with an R2 value of 0.8632 and RMSE of 40.90, and when optimized, the hybrid XGBoost models showed superior performance, with XGBoost-SMA achieving the highest accuracy. The results revealed that the XGBoost-SMA model achieved the most desired accuracy with an R2 value of 0.9713 and a root mean square error of 18.73 for the test dataset. This research highlights machine learning’s applicability in hydropower prediction and suggests hybrid models as a promising approach for better accuracy, emphasizing XGBoost’s potential in efficient hydropower forecasting to meet global electricity demands. |
| format | Article |
| id | doaj-art-86d436fe4a704ca08b4bf13b4c0db3bd |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-86d436fe4a704ca08b4bf13b4c0db3bd2025-01-17T04:49:24ZengElsevierAin Shams Engineering Journal2090-44792025-01-01161103206Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniquesZhenya Qi0Yudong Feng1Shoufeng Wang2Chao Li3Corresponding author.; Shandong Electric Power Engineering Consulting Institute Corr. LTD. Jinan, Shandong 250014 ChinaShandong Electric Power Engineering Consulting Institute Corr. LTD. Jinan, Shandong 250014 ChinaShandong Electric Power Engineering Consulting Institute Corr. LTD. Jinan, Shandong 250014 ChinaShandong Electric Power Engineering Consulting Institute Corr. LTD. Jinan, Shandong 250014 ChinaHydropower plays a crucial role in electricity generation, contributing over 60% of total renewable energy output. Its ability to stabilize energy fluctuations makes it essential in green energy initiatives. Accurate prediction of hydropower production is vital, considering its dependence on various factors like weather, water storage, and electricity generation. Traditional methods struggle with the complexities involved. This study utilized Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) algorithms, both individually and in hybrid models enhanced by optimization techniques like Slime Mould Algorithm (SMA), Aquila Optimizer (AO), and Grey Wolf Optimization (GWO). XGBoost outperformed SVR in single model predictions with an R2 value of 0.8632 and RMSE of 40.90, and when optimized, the hybrid XGBoost models showed superior performance, with XGBoost-SMA achieving the highest accuracy. The results revealed that the XGBoost-SMA model achieved the most desired accuracy with an R2 value of 0.9713 and a root mean square error of 18.73 for the test dataset. This research highlights machine learning’s applicability in hydropower prediction and suggests hybrid models as a promising approach for better accuracy, emphasizing XGBoost’s potential in efficient hydropower forecasting to meet global electricity demands.http://www.sciencedirect.com/science/article/pii/S2090447924005872Hydropower Generation predictionMachine learningSupport vector regressionExtreme Gradient BoostingOptimization Methods |
| spellingShingle | Zhenya Qi Yudong Feng Shoufeng Wang Chao Li Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques Ain Shams Engineering Journal Hydropower Generation prediction Machine learning Support vector regression Extreme Gradient Boosting Optimization Methods |
| title | Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques |
| title_full | Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques |
| title_fullStr | Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques |
| title_full_unstemmed | Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques |
| title_short | Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques |
| title_sort | enhancing hydropower generation predictions a comprehensive study of xgboost and support vector regression models with advanced optimization techniques |
| topic | Hydropower Generation prediction Machine learning Support vector regression Extreme Gradient Boosting Optimization Methods |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005872 |
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