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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Rafael Ninno Muniz,
  • William Gouvêa Buratto,
  • Gabriel Villarrubia Gonzalez,
  • Laio Oriel Seman,
  • Valdeci José Costa,
  • Ademir Nied

摘要

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.