Electricity demand forecasting 2030 by decomposition analysis of open data

dc.contributor.affiliationLUT University
dc.contributor.affiliationLUT University
dc.contributor.affiliationLUT University
dc.contributor.affiliationLUT University
dc.contributor.affiliationLUT University
dc.contributor.authorRäisänen, Otto
dc.contributor.authorHaakana, Juha
dc.contributor.authorHaapaniemi, Jouni
dc.contributor.authorLassila, Jukka
dc.contributor.authorPartanen, Jarmo
dc.contributor.countryFinland
dc.contributor.countryFinland
dc.contributor.countryFinland
dc.contributor.countryFinland
dc.contributor.countryFinland
dc.contributor.detailedauthorRäisänen, Otto , LUT University, Finland
dc.contributor.detailedauthorHaakana, Juha, LUT University, Finland
dc.contributor.detailedauthorHaapaniemi, Jouni, LUT University, Finland
dc.contributor.detailedauthorLassila, Jukka , LUT University, Finland
dc.contributor.detailedauthorPartanen, Jarmo, LUT University, Finland
dc.date.accessioned2019-07-24T12:43:50Z
dc.date.available2019-07-24T12:43:50Z
dc.date.conferencedate3-6 June 2019
dc.date.issued2019-06-03
dc.description.abstractThe demand of electrical energy in the household sectorfollowed a nearly linear growth trend for a long timemaking demand forecasting relatively simple. However, inthe last decade the growth has stalled due to energyefficiency policies, structural changes in the society andemergence of new technologies. In sparsely populatedareas the population is continually declining which affectselectrical energy consumption and increases averageconductor length per customer. These changes in theoperational environment pose challenges to demandforecasting. Historical data relating to the change factorscould be used to improve demand forecasts. This studyintroduces a method that uses decomposition and timeseriesanalysis of open data to forecast future electricalenergy demand. The method is used to forecast theelectrical energy consumption for the household sector ina group of Finnish municipalities which have a decliningpopulation.
dc.description.conferencelocationMadrid, Spain
dc.description.conferencenameCIRED 2019
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionPlanning of power distribution systems
dc.description.sessionidSession 5
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/557
dc.identifier.urihttp://dx.doi.org/10.34890/780
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleElectricity demand forecasting 2030 by decomposition analysis of open data
dc.title.number1756
dc.typeConference Proceedings
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