Water level forecasting for hydroelectric power plants using deep learning

As global dependence on renewable energy intensifies, accurate water-level forecasts are essential for reliable power generation, reservoir management, and flood mitigation in hydroelectric power plants. However, existing models struggle to capture the nonlinear dynamics of hydrological time series without extensive manual tuning. We propose a novel end-to-end fully autonomous forecasting workflow that combines the Time-series Dense Encoder architecture with the Non-dominated Sorting Genetic Algorithm III. Our method reduces tuning time from weeks to ∼17 h while concurrently analyzing hyperparameter importance through functional ANOVA, providing actionable insight for practitioners. Evaluation on three real-world datasets that span large, medium, and small storage capacities shows that the proposed model outperforms several state-of-the-art machine learning methods across all evaluated scenarios, and improves forecasting accuracy by up to 44.16% over the baseline implementation. Furthermore, due to its adaptable design, our workflow can seamlessly adjust to various forecasting tasks and datasets with no manual intervention, highlighting the effectiveness and practicality of our proposed methodology for enhancing water level forecasting accuracy.