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Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction.

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Bibliographic Details
Title: Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction.
Authors: Seman, Laio Oriel1 (AUTHOR) laio.seman@ufsc.br, Yow, Kin-Choong2 (AUTHOR) Kin-Choong.Yow@uregina.ca, Stefenon, Stefano Frizzo2,3 (AUTHOR) stefano.stefefenon@isel.br
Source: Electric Power Systems Research. Jun2026, Vol. 255, pN.PAG-N.PAG. 1p.
Subjects: Transformer models, Forecasting, Deep learning, Electric power system planning, Ensemble learning, Wavelet transforms
Geographic Terms: Brazil
Abstract: • Hybrid Transformer with sparse Mixture of Experts for reservoir forecasting. • Adaptive multi-head preprocessing captures nonstationary temporal patterns. • Empirical Wavelet Transform improves denoising and short-term accuracy. • Tested on 19 interconnected reservoirs using real operational data. • Outperforms 18 state-of-the-art models with lower prediction error. In hydroelectric-based systems, effective energy generation planning relies heavily on precise forecasting of reservoir water levels. This paper proposes a novel hybrid forecasting framework that integrates multiple preprocessing strategies with a sparse Mixture of Experts enhanced Transformer architecture for short-term reservoir volume prediction. When evaluated on 19 interconnected reservoirs across two major river basins in southern Brazil using real operational data from the Brazilian National System Operator, the proposed model achieves a mean squared error of 0.062 and a mean absolute error of 0.145. Comprehensive benchmarking against 18 state-of-the-art deep learning methods demonstrates that the proposed approach significantly outperforms existing methods while maintaining computational efficiency through sparse expert routing. Our results confirm that combining diverse preprocessing strategies with conditional computation mechanisms provides superior forecasting accuracy for reservoir management in hydroelectric power systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Hybrid Transformer with sparse Mixture of Experts for reservoir forecasting. • Adaptive multi-head preprocessing captures nonstationary temporal patterns. • Empirical Wavelet Transform improves denoising and short-term accuracy. • Tested on 19 interconnected reservoirs using real operational data. • Outperforms 18 state-of-the-art models with lower prediction error. In hydroelectric-based systems, effective energy generation planning relies heavily on precise forecasting of reservoir water levels. This paper proposes a novel hybrid forecasting framework that integrates multiple preprocessing strategies with a sparse Mixture of Experts enhanced Transformer architecture for short-term reservoir volume prediction. When evaluated on 19 interconnected reservoirs across two major river basins in southern Brazil using real operational data from the Brazilian National System Operator, the proposed model achieves a mean squared error of 0.062 and a mean absolute error of 0.145. Comprehensive benchmarking against 18 state-of-the-art deep learning methods demonstrates that the proposed approach significantly outperforms existing methods while maintaining computational efficiency through sparse expert routing. Our results confirm that combining diverse preprocessing strategies with conditional computation mechanisms provides superior forecasting accuracy for reservoir management in hydroelectric power systems. [ABSTRACT FROM AUTHOR]
ISSN:03787796
DOI:10.1016/j.epsr.2026.112754