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Deriving Operating Rules of Hydropower Reservoirs Using Multi-Strategy Ensemble Henry Gas Solubility Optimization-Driven Support Vector Machine
Hydropower is an important clean renewable energy that plays a key role in coping with issues such as global energy security, environmental protection, and climate change. In order to improve the optimal operation ability of hydropower reservoirs in the context of forecast runoff with limited accuracy and prediction period, there has been a growing interest in deriving operating rules of hydropower reservoirs. Reasonable operation decision is very important for safe operation of reservoirs and efficient utilization of water resources. Therefore, a novel method of operation rules derivation is proposed in this study. Optimal operation model of hydropower reservoir is established and support vector machine (SVM) is used to derive operation rules based on the optimal operation results. In order to improve the performance of SVM, the Henry gas solubility optimization (HGSO) is used to optimize its hyperparameters for the first time. Meanwhile, multiple strategies are applied to overcome the drawbacks of HGSO. The multi-verse optimizer (MVO) is used to enhance the exploration capability of basic HGSO. Quadratic interpolation (QI) is used to improve the exploitation ability of HGSO. In this study, the Xiluodu and Xiangjiaba hydropower reservoirs in the upper Yangtze River of China were selected as a case study. First, the improved HGSO called MVQIHGSO was tested on 23 classical benchmark functions. Then, it was employed to optimize hyperparameters of SVM model for deriving operation rules. The results and statistical studies indicate that the improved HGSO outperforms the comparison algorithms in exploration and exploitation. The obtained results imply that the novel method named MVQIHGSO-SVM can provide a new practical tool to deriving operation rules for hydropower reservoirs, which is conducive to the safe and efficient utilization of water resources.
Deriving Operating Rules of Hydropower Reservoirs Using Multi-Strategy Ensemble Henry Gas Solubility Optimization-Driven Support Vector Machine
Hydropower is an important clean renewable energy that plays a key role in coping with issues such as global energy security, environmental protection, and climate change. In order to improve the optimal operation ability of hydropower reservoirs in the context of forecast runoff with limited accuracy and prediction period, there has been a growing interest in deriving operating rules of hydropower reservoirs. Reasonable operation decision is very important for safe operation of reservoirs and efficient utilization of water resources. Therefore, a novel method of operation rules derivation is proposed in this study. Optimal operation model of hydropower reservoir is established and support vector machine (SVM) is used to derive operation rules based on the optimal operation results. In order to improve the performance of SVM, the Henry gas solubility optimization (HGSO) is used to optimize its hyperparameters for the first time. Meanwhile, multiple strategies are applied to overcome the drawbacks of HGSO. The multi-verse optimizer (MVO) is used to enhance the exploration capability of basic HGSO. Quadratic interpolation (QI) is used to improve the exploitation ability of HGSO. In this study, the Xiluodu and Xiangjiaba hydropower reservoirs in the upper Yangtze River of China were selected as a case study. First, the improved HGSO called MVQIHGSO was tested on 23 classical benchmark functions. Then, it was employed to optimize hyperparameters of SVM model for deriving operation rules. The results and statistical studies indicate that the improved HGSO outperforms the comparison algorithms in exploration and exploitation. The obtained results imply that the novel method named MVQIHGSO-SVM can provide a new practical tool to deriving operation rules for hydropower reservoirs, which is conducive to the safe and efficient utilization of water resources.
Deriving Operating Rules of Hydropower Reservoirs Using Multi-Strategy Ensemble Henry Gas Solubility Optimization-Driven Support Vector Machine
Hongya Qiu (author) / Ting Hu (author) / Song Zhang (author) / Yangfan Xiao (author)
2023
Article (Journal)
Electronic Resource
Unknown
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