Research on intelligent day-ahead hydropower generation ability forecasting method

Accurate prediction of hydropower generation capacity is of great significance for the coordinated operation of basin cascade hydropower stations. To address the challenge that conventional prediction methods struggle to effectively integrate the spatiotemporal coupling effects of multidimensional features, this paper proposes an intelligent day-ahead hydropower generation ability forecasting method. The approach involves: screening key factors through a five-stage feature identification strategy, constructing an Autoformer-based intelligent prediction model, and reducing forecast errors via an error correction strategy, thereby achieving long-term hydropower generation forecasting. Engineering applications demonstrate that the proposed method outperforms conventional models in key performance metrics. For instance, at a 1-day forecast horizon, the root mean square error (RMSE) at Chitan Reservoir is reduced by 9.8% compared to the suboptimal model, while the Nash-Sutcliffe efficiency (NSE) at Dayan Reservoir improves by 4.8%. Moreover, the model maintains superior performance even with extended forecast horizons, confirming its high robustness in long-term sequence prediction. Through systematic innovations in feature engineering, intelligent prediction modeling, and error suppression strategies, this study provides high-precision, long-term decision support for intelligent cascade hydropower scheduling.