Research on a Data-Driven Decision-Making Method for Hydropower Unit Commitment

In order to address the challenges of solving integer variables and the low decision-making efficiency in hydropower unit commitment optimization, this study proposed a data-driven solution method. First, by analyzing the temporal characteristics of daily hydropower output and the regularity of unit commitment encoding, a supervised classification model based on the Seq2Seq framework was developed. The model was tested using data from the Xiangjiaba Hydropower Station with 8 units. The results indicated that data clustering significantly improves learning accuracy. When output fluctuations are small, the model achieves an accuracy above 0.9. However, with greater fluctuations, the accuracy drops to around 0.7~0.8. The learning performance is also influenced by the number of training samples and categories, and the difficulty increases during load-peaking periods.