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dc.contributor.authorGuerra, Michele
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorScardapane, Simone
dc.date.accessioned2024-01-10T09:54:08Z
dc.date.available2024-01-10T09:54:08Z
dc.date.issued2023-12-15
dc.description.abstractSome applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work focuses on reservoir computing as the core time series forecasting method, due to its computational efficiency and effectiveness in predicting time series. While the reservoir computing literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting. Both Bayesian and deterministic approaches to uncertainty assessment are evaluated and compared in terms of their prediction accuracy, computational resource efficiency and reliability of the estimated uncertainty, based on a set of carefully chosen performance metrics.en_US
dc.identifier.citationGuerra M, Bianchi FM, Scardapane S. Probabilistic Load Forecasting With Reservoir Computing. IEEE Access. 2023;11en_US
dc.identifier.cristinIDFRIDAID 2222603
dc.identifier.doi10.1109/ACCESS.2023.3343467
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10037/32397
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Access
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleProbabilistic Load Forecasting With Reservoir Computingen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)