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Patch-based transformer with mixture of experts and conformal inference for curtailment time series forecasting

2026Artigo de investigação Acesso aberto
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dc.contributor.authorHeidrich, Mikhail Zimmer
dc.contributor.authorReis, Mari Aurora Favero
dc.contributor.authorYamaguchi, Cristina Keiko
dc.contributor.authorSeman, Laio Oriel
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.editorIEEE
dc.date.accessioned2026-09-23T14:38:25Z
dc.date.available2026-09-23T14:38:25Z
dc.date.issued2026
dc.description.abstractThe growth of variable renewable energy sources has increased the occurrence of curtailment in power systems, particularly in regions affected by transmission constraints and limited operational flexibility. Reliable forecasting of curtailment time series, together with well-calibrated uncertainty estimates, is therefore essential to support operational planning, market decisions, and risk management. This paper proposes a curtailment forecasting framework that combines a high-capacity neural time series model with distribution-free conformal prediction to jointly address accuracy and uncertainty quantification. The forecasting backbone is a Transformer-based encoder-decoder architecture enhanced with reversible instance normalization to mitigate distribution shift, patch-based tokenization to reduce sequence length and improve temporal representation, and a Mixture of Experts feed-forward module to enable conditional computation and improved generalization. The proposed method is evaluated against a wide range of state-of-the-art models, including NBEATS, NBEATSx, NHITS, PatchTST, LSTM, GRU, TFT, Informer, and Autoformer, across multiple forecasting horizons. The model achieves an MSE of 0.0059 for a horizon equal to 3h, corresponding to a 13.2% reduction relative to the second-best methods. For a horizon equal to 5h, the MSE is reduced to 0.0119, representing a 29.6% improvement, while for a horizon equal to 10, the model attains an MSE of 0.0461, outperforming NHITS by 22.9%. Ablation analysis confirms the importance of patch embedding, the Mixture of Experts mechanism, and reversible instance normalization. The conformal prediction module achieves an empirical coverage of 91.27% for a nominal 90% level, with adaptive and practically useful interval widths.eng
dc.identifier.citationHeidrich, M. Z., Reis, M. A. F., Yamaguchi, C. K., Seman, L. O., & Stefenon, S. F. (2026). Patch-based transformer with mixture of experts and conformal inference for curtailment time series forecasting. IEEE Access, 14, 64144–64160. https://doi.org/10.1109/ACCESS.2026.3686958
dc.identifier.doi10.1109/ACCESS.2026.3686958
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/23182
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationFAPESC
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/11493890
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCurtailment forecasting
dc.subjectTime series prediction
dc.subjectTransformer models
dc.subjectMixture of experts
dc.subjectConformal prediction
dc.titlePatch-based transformer with mixture of experts and conformal inference for curtailment time series forecastingeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage64160
oaire.citation.startPage64144
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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