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Load forecasting in electrical distribution: grid of medium voltage

dc.contributor.authorChemetova, Svetlana
dc.contributor.authorSantos, Paulo
dc.contributor.authorVentim-Neves, Mário
dc.date.accessioned2016-04-26T13:10:24Z
dc.date.available2016-04-26T13:10:24Z
dc.date.issued2016-04-12
dc.descriptionTrabalho apresentado no 7th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS’16), 11-13 abril de 2016, Caparica, Portugalpt_PT
dc.description.abstractThe importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 hours up to a week. The forecast method is based on the combined use of a regression model and artificial neural networks (ANN). The study was done with the time series of telemetry data of the DSO (EDP Distribution) and climatic records from IPMA (Portuguese Institute of Sea and Atmosphere), applied for the urban area of Évora - one of the first Smart Cities in Portugal. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The error (MAPE) was lower than 5%, meaning that chosen methodology clearly validate the feasibility of the test.pt_PT
dc.identifier.doi10.1007/978-3-319-31165-4_33
dc.identifier.urihttp://hdl.handle.net/10400.26/13309
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectElectric power systemspt_PT
dc.subjectload forecastingpt_PT
dc.subjectsmart-gridspt_PT
dc.subjectdistribution systemspt_PT
dc.subjectelectric substationspt_PT
dc.subjectartificial neural networkspt_PT
dc.titleLoad forecasting in electrical distribution: grid of medium voltagept_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceCaparica, Portugalpt_PT
oaire.citation.title7th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS’16)pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

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