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Multi-scale convolutional Chebyshev Kolmogorov-Arnold networks using conformal prediction for photovoltaic power forecasting

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorTakara, Lucas de Azevedo
dc.contributor.authorSouza, Rodrigo Clemente Thom de
dc.contributor.authorMariani, Viviana Cocco
dc.contributor.authorCoelho, Leandro Dos Santos
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.editorIEEE
dc.date.accessioned2026-09-23T11:01:44Z
dc.date.available2026-09-23T11:01:44Z
dc.date.issued2026
dc.description.abstractAccurate photovoltaic (PV) power forecasting remains challenging due to nonlinear dynamics, rapid weather variations, and multi-scale temporal dependencies. This paper proposes ConvChebyKAN, a hybrid deep learning architecture that integrates multi-scale causal convolutions, Chebyshev polynomial parameterized Kolmogorov Arnold networks (KANs), and conformal prediction to improve both deterministic and probabilistic forecasting performance. The multi-scale convolutional module captures short-term fluctuations and long-term temporal dependencies, while the Chebyshev-based KAN improves nonlinear representation capability and numerical stability. A multi-objective hyperparameter optimization strategy based on Pareto efficiency and TOPSIS ranking is used to jointly optimize forecasting accuracy, prediction interval sharpness, and coverage reliability. The model is evaluated using real photovoltaic generation and meteorological data from four seasonal datasets and forecasting horizons from 1 to 48 hours ahead. ConvChebyKAN reaches an MAE of 149.13 kWh, RMSE of 227.34 kWh, R2 of 98.2%, and sMAPE of 6.21%, outperforming KAN and other machine learning models. These results show that the proposed architecture effectively captures multi-scale temporal patterns while providing reliable uncertainty estimates, supporting PV forecasting applications in power system operation and energy management.eng
dc.identifier.citationTakara, L. de A., Souza, R. C. T. de, Mariani, V. C., Coelho, L. dos S., & Stefenon, S. F. (2026). Multi-scale convolutional Chebyshev Kolmogorov-Arnold networks using conformal prediction for photovoltaic power forecasting. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3694780
dc.identifier.doi10.1109/ACCESS.2026.3694780
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/23180
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/11526791
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectConformal prediction
dc.subjectDeep learning
dc.subjectKolmogorov-Arnold networks
dc.subjectMulti-objective optimization
dc.subjectPhotovoltaic power forecasting
dc.titleMulti-scale convolutional Chebyshev Kolmogorov-Arnold networks using conformal prediction for photovoltaic power forecastingeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage81194
oaire.citation.startPage81170
oaire.citation.titleIEEE Access
oaire.citation.volume14
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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ISEL - Eng. Elect. Tel. Comp. - Artigos