Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants

Accurate forecasting of reservoir levels in hydroelectric power plants is essential for efficient energy generation, operational safety, and sustainable water management. This study proposes a hybrid forecasting framework that integrates seasonal-trend decomposition using loess (STL) with the neural hierarchical interpolation time series (NHITS) model optimized through multi-agent hyperparameter optimization (HPO). The STL filter is employed to remove high-frequency noise and preserve underlying signal trends. NHITS leverages hierarchical multi-scale processing and interpolation-based reconstruction to capture both short- and long-term temporal dependencies, while the multi-agent HPO ensures optimal hyperparameter configuration. The proposed method was evaluated using turbine flow data from the Santo Antônio hydroelectric power plant in Brazil, achieving superior performance compared to state-of-the-art benchmarks across very short- and short-term forecasting horizons.