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Optimized Harmonic-Multiscale Kolmogorov–Arnold Network denoised by EWT for hydroelectric dam useful volume forecast

2026Artigo de investigação Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorSeman, Laio Oriel
dc.contributor.authorMatos-Carvalho, João Pedro
dc.contributor.authorNied, Ademir
dc.contributor.authorGonzalez, Gabriel Villarrubia
dc.contributor.authorYow, Kin-Choong
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.editorElsevier
dc.date.accessioned2026-09-23T10:31:41Z
dc.date.available2026-09-23T10:31:41Z
dc.date.issued2026
dc.description.abstractAccurate 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.eng
dc.identifier.citationStefenon, 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
dc.identifier.doi10.1016/j.energy.2026.141214
dc.identifier.eissn1873-6785
dc.identifier.urihttp://hdl.handle.net/10400.21/23179
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationUID/00408/2025; UID/PRR/00408/2025
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0360544226013204?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectHydroelectric power
dc.subjectReservoir forecasting
dc.subjectKolmogorov–Arnold Network
dc.subjectTime series analysis
dc.subjectHarmonic-multiscale modeling
dc.titleOptimized Harmonic-Multiscale Kolmogorov–Arnold Network denoised by EWT for hydroelectric dam useful volume forecasteng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue141214
oaire.citation.titleEnergy
oaire.citation.volume356
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameInstituto Politécnico de Lisboa, Instituto Superior de Engenharia de Lisboa
person.familyNameStefenon
person.givenNameStefano Frizzo
person.identifier.orcidhttps://orcid.org/0000-0002-3723-616X
relation.isAuthorOfPublicationb0383f14-261a-42cf-a223-7c571c84a241
relation.isAuthorOfPublication.latestForDiscoveryb0383f14-261a-42cf-a223-7c571c84a241

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