Wind power and electricity consumption forecasting on a smart house location

dc.contributor.authorEliasstam, H.
dc.contributor.authorGenikomsakis, Konstantinos N.
dc.contributor.authorIoakimidis, Christos S.
dc.date.accessioned2026-09-26T07:19:18Z
dc.date.available2026-09-26T07:19:18Z
dc.date.issued2013-03
dc.date.updated2026-09-26T07:19:18Z
dc.description.abstractThis paper presents the use of an artificial neural network for classification on a residence house that uses wind and electricity consumption predictions to identify patterns at the desired location, in order to obtain a stochastic distribution of the daily wind and electricity profile. This is a step on the further creation of a short-term operation model that allows determining the technical and economic impact of stationary/mobile batteries of electric vehicles in presence of microrenewables along with the electricity consumption. This short-term operation model will be in the day-ahead perfect market operation (unit commitment) where specific changes are made to consider stationary and mobile operation.en
dc.identifier.citationEliasstam, Genikomsakis, & Ioakimidis. (2013). Wind power and electricity consumption forecasting on a smart house location. Renewable Energy and Power Quality Journal, 1(11), 655-659. https://doi.org/10.24084/REPQJ11.404
dc.identifier.doi10.24084/REPQJ11.404
dc.identifier.eissn2172-038X
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6698
dc.language.isoeng
dc.publisherEuropean Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)
dc.subject.otherArtificial neural networks
dc.subject.otherElectricity
dc.subject.otherForecasting
dc.subject.otherSmart homes
dc.subject.otherWind energy
dc.titleWind power and electricity consumption forecasting on a smart house locationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage659
oaire.citation.issue11
oaire.citation.startPage655
oaire.citation.titleRenewable Energy and Power Quality Journal
oaire.citation.volume1
oaire.versionVoR
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