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Optimized Harmonic-Multiscale Kolmogorov–Arnold Network denoised by EWT for hydroelectric dam useful volume forecast
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Orientador(es)
Resumo(s)
Accurate forecasting of the useful volume of a hydroelectric reservoir is essential for energy planning and water resource management. To address this challenge, this paper proposes a novel Harmonic-Multiscale Kolmogorov–Arnold Network forecasting model. The model integrates sinusoidal (Sin) basis functions to model global oscillatory patterns and wavelet (Wav) bases to capture localized transient features within the Kolmogorov Arnold Network (KAN) framework (SinWavKAN). This hybrid representation enhances feature expressiveness by jointly modeling smooth long-term dependencies and short-term irregular dynamics. The input signals are first denoised using the Empirical Wavelet Transform (EWT) to isolate informative modes from noise, and the SinWavKAN’s hyperparameters are optimized via an Adaptive Tree-structured Parzen Estimator (ATPE). An ablation study confirmed the synergistic superiority of the sinusoidal-wavelet combination over other basis functions. Compared to state-of-the-art KAN variants (e.g., fastKAN, PyKAN, WavKAN), the
proposed architecture, named in short Opt-EWT-SinWavKAN, demonstrated a reduction in mean absolute error by approximately 85%–93% across forecasting horizons from 1 to 30 days. The results conclusively show that the Opt-EWT-SinWavKAN provides a more reliable approach for hydroelectric reservoir volume forecasting.
Descrição
Palavras-chave
Hydroelectric power Reservoir forecasting Kolmogorov–Arnold Network Time series analysis Harmonic-multiscale modeling
Contexto Educativo
Citação
Stefenon, S. F., Seman, L. O., Matos-Carvalho, J. P., Nied, A., Villarrubia Gonzalez, G., & Yow, K.-C. (2026). Optimized harmonic-multiscale Kolmogorov–Arnold network denoised by EWT for hydroelectric dam useful volume forecast. Energy, 356, Article 141214. https://doi.org/10.1016/j.energy.2026.141214
Editora
Elsevier