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Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants
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  • Published: 10 January 2026

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

  • Rafael Ninno Muniz1,
  • William Gouvêa Buratto2,3,
  • Gabriel Villarrubia Gonzalez3,
  • Laio Oriel Seman4,
  • Valdeci José Costa5 &
  • …
  • Ademir Nied2 

Scientific Reports , Article number:  (2026) Cite this article

  • 658 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering
  • Mathematics and computing

Abstract

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.

Data availability

For future comparisons, the dataset is available at: https://dados.ons.org.br/dataset/dados_hidrologicos_ho.

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Funding

This work was supported by the project Self-adaptive platform based on intelligent agents for the optimization and management of operational processes in logistic warehouses (PLAUTON), PID2023-151701OB-C21, funded by MCIN/AEI/10.13039/501100011033/FEDER, EU. Buratto would like to thank UDESC for the financial support for his interuniversity exchange doctorate at the University of Salamanca. The authors would like to thank the Carlos Chagas Filho Foundation for Research Support in the Fluminense Federal University (UFF). Also, the authors would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES - Brazil) for the scholarship of the second author and by Council for Scientific and Technological Development (CNPq) for the grant of the last author. This study was financed (i) in part by CAPES under the doctoral scholarship number 88887.808258/2023-00, and (ii) by CNPq under grant number 307858/2025-1. The authors would also like to thank researcher Stefano Stefenon for his valuable contributions to this research.

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Authors and Affiliations

  1. Production Engineering Graduate Program, Department of Science and Technology, Federal Fluminense University (UFF), Rio das Ostras, Brazil

    Rafael Ninno Muniz

  2. Systems Control Research Group, Department of Electrical Engineering, Santa Catarina State University (UDESC), 89219-710, Joinville, SC, Brazil

    William Gouvêa Buratto & Ademir Nied

  3. Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Salamanca, 37008, Spain

    William Gouvêa Buratto & Gabriel Villarrubia Gonzalez

  4. Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil

    Laio Oriel Seman

  5. Environmental Engineering Graduate Program, Department of Environmental Engineering, Santa Catarina State University (UDESC), Lages, 88520-000, Brazil

    Valdeci José Costa

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Contributions

Rafael Ninno Muniz and William Gouvêa Buratto - wrote the introduction, method, results, and conclusion sections, drew the figures, and computed the experiments. Gabriel Villarrubia Gonzalez, Laio Oriel Seman, Valdeci José Costa, and Ademir Nied - proofread and supervised.

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Correspondence to Rafael Ninno Muniz.

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Muniz, R.N., Buratto, W.G., Gonzalez, G.V. et al. Optimized hybrid neural hierarchical interpolation time series with STL for flow forecasting in hydroelectric power plants. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34847-x

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  • Received: 13 October 2025

  • Accepted: 31 December 2025

  • Published: 10 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-34847-x

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Keywords

  • Time series forecasting
  • Signal denoising
  • Seasonal-trend decomposition using loess
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