Multi-step machine learning forecasting of power consumption and pv generation for distributed energy management applications

dc.contributor.author	Pasarín, O.
dc.contributor.author	García Fernández, Pablo
dc.contributor.author	González, L.
dc.contributor.author	Villa, G.
dc.date.accessioned	2024-07-11T07:34:08Z
dc.date.available	2024-07-11T07:34:08Z
dc.date.issued	2023
dc.identifier.isbn	979-835031644-5
dc.identifier.uri	https://hdl.handle.net/10651/73682
dc.description.sponsorship	This work was supported in part by the Spanish Ministry of Innovation and Science under Grant MCINN-22-TED2021-129796B-C21 and by the Principality of Asturias, FICYT, FEDER funds under Grant SV-PA-21-AYUD/2021/57546.
dc.format.extent	p. 1472-1479
dc.language.iso	eng
dc.relation.ispartof	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182917125&doi=10.1109%2fECCE53617.2023.10362169&partnerID=40&md5=bcf4f69579c5fc3684694d7dd2bb7f55
dc.title	Multi-step machine learning forecasting of power consumption and pv generation for distributed energy management applications
dc.type	conference output
dc.identifier.doi	10.1109/ECCE53617.2023.10362169
dc.relation.publisherversion	http://dx.doi.org/10.1109/ECCE53617.2023.10362169

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