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Time series forecasting based on multi-criteria optimization for model and filter selection applied to hydroelectric power plants

Author

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  • Muniz, Rafael Ninno
  • Stefenon, Stefano Frizzo
  • Buratto, William Gouvêa
  • Nied, Ademir
  • Cardoso, Rodolfo
  • Yamaguchi, Cristina Keiko
  • Yow, Kin-Choong

Abstract

Power generation management in water-resource-based power systems depends on reservoir levels at hydroelectric power plants. Considering the advances in machine learning, forecasting inflow variation using time series could be an alternative for improving power system management. Given that there are several forecasting models and filters that can be applied, choosing one can be a challenging task, requiring experience from the designer. To solve this, multi-criteria optimization for selecting the models using the tree-structured Parzen estimator approach is proposed in this paper. The study considers the inflow data from the Belo Monte dam in Brazil. The multi-layer Elman recurrent neural network (RNN), dilated RNN, long short-term memory (LSTM), temporal fusion transformer (TFT), temporal convolutional neural (TCN), deep non-parametric time series (DeepNPTS), neural basis expansion analysis for time series (N-BEATS), and neural hierarchical interpolation for time series (NHITS) models are considered. The Christiano-Fitzgerald, Hodrick–Prescott, season-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL filters are used. The proposed method, based on hypertuned TFT with the Hodrick–Prescott filter, had a mean absolute percentage error (MAPE) of 0.02 and a symmetric MAPE of 1.99, being superior to all the compared structures.

Suggested Citation

  • Muniz, Rafael Ninno & Stefenon, Stefano Frizzo & Buratto, William Gouvêa & Nied, Ademir & Cardoso, Rodolfo & Yamaguchi, Cristina Keiko & Yow, Kin-Choong, 2025. "Time series forecasting based on multi-criteria optimization for model and filter selection applied to hydroelectric power plants," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225043300
    DOI: 10.1016/j.energy.2025.138688
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