A runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model - Wu, Yang; Xie, Yigong; Xu, Fengjiao; Zhu, Xinchun; Liu, Shuangquan (Frontiers in Energy Research, 2024)
This paper proposes a runoff-based hydroelectricity prediction method using meteorological similar days and the XGBoost model. Accurately predicting hydroelectricity supply and demand is critical for conserving resources, ensuring power supply, and mitigating the impact of natural disasters. To achieve this, historical meteorological and runoff data are analyzed to select meteorological data similar to the current date, forming a meteorological similar day dataset. The XGBoost model is then trained and used to predict the meteorological similar day dataset and obtain hydroelectricity prediction results. To evaluate the proposed method, the hydroelectricity cluster in Yunnan, China is used as sample data. The results show that the method exhibits high prediction accuracy and stability, providing an effective approach to hydroelectricity prediction. This study demonstrates the potential of using meteorological similar days and the XGBoost model for hydroelectricity prediction and highlights the importance of accurate hydroelectricity prediction for water resource management and electricity production.