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Decomposition-driven Mamba state space models with expert routing for wind curtailment forecasting

2026Artigo de investigação Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorSeman, Laio Oriel
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.authorMatos-Carvalho, João Pedro
dc.contributor.authorStefenon, Stefano Frizzo
dc.contributor.editorElsevier
dc.date.accessioned2026-09-23T09:33:07Z
dc.date.available2026-09-23T09:33:07Z
dc.date.issued2026
dc.description.abstractWind power curtailment, known in Brazil as constrained-off, has intensified with the rapid expansion of renew able generation, driven by transmission operational constraints. Accurate forecasting of curtailment is essential for grid planning, compensation mechanisms, and infrastructure optimization. However, constrained-off time series exhibit strong non-stationarity, multi-scale behavior, and long-range dependencies that challenge conventional forecasting models. This paper proposes a decomposition-driven Mamba state space framework with Mixture-of-Experts (MoE) routing for wind curtailment forecasting. The approach integrates Entropy-Guided Variational Mode Decomposition (EG-VMD) for adaptive trend and noise separation, Reversible Instance Normalization to mitigate distribution shift, multi-scale seasonal-trend modeling, and a sparse expert-based Mamba backbone with linear-time complexity. In benchmark comparisons against state-of-the-art forecasting models, the proposed MoE-Mamba reduces Mean Squared Error (MSE) by up to 27%, Root Mean Squared Error by 14.6%, and Mean Absolute Error by 16.1% relative to the strongest baselines, while improving upon a single-expert Mamba by 15.6% in MSE. The results highlight the effectiveness of decomposition-enhanced State Space Models with expert routing for scalable, long-horizon forecasting in renewable power systems.eng
dc.identifier.citationSeman, L. O., Stefenon, S. F., & Matos-Carvalho, J. P. (2026). Decomposition-driven Mamba state space models with expert routing for wind curtailment forecasting. Electric Power Systems Research, 260, Article 113259. https://doi.org/10.1016/j.epsr.2026.113259
dc.identifier.doi10.1016/j.epsr.2026.113259
dc.identifier.eissn1873-2046
dc.identifier.urihttp://hdl.handle.net/10400.21/23178
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationUID/PRR/00408/2025; 2023.15441.TENURE.051/CP00003/ CT00029; UID/00408/2025,
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0378779626005523?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMamba architecture
dc.subjectMixture-of-experts
dc.subjectReversible instance normalization
dc.subjectVariational mode decomposition
dc.subjectWind curtailment
dc.titleDecomposition-driven Mamba state space models with expert routing for wind curtailment forecastingeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue113259
oaire.citation.titleElectric Power Systems Research
oaire.citation.volume260
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