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dc.contributor.authorFoldvik Eikeland, Odin
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorChiesa, Matteo
dc.contributor.authorApostoleris, Harry
dc.contributor.authorHansen, Morten
dc.contributor.authorChiou, Yu-Cheng
dc.date.accessioned2021-07-07T11:58:31Z
dc.date.available2021-07-07T11:58:31Z
dc.date.issued2021-02-03
dc.description.abstractForecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at the end of the radial distribution network without alternative energy supply. Therefore, it is crucial to develop an accurate forecasting model to manage and optimize the limited energy resources available. We first compare the accuracy of several models that perform short-and medium-term load forecasts in rural areas, where a single industrial customer dominates the electricity consumption. We consider both statistical methods and machine learning models to predict energy demand. Then, we evaluate the transferability of each method to a geographical rural area different from the one considered for training. Our results indicate that statistical models achieve higher accuracy on longer forecast horizons relative to neural networks, while the machine-learning approaches perform better in predicting load at shorter time intervals. The machine learning models also exhibit good transferability, as they manage to predict well the load at new locations that were not accounted for during training. Our work will serve as a guide for selecting the appropriate prediction model and apply it to perform energy load forecasting in rural areas and in locations where historical consumption data may be limited or even not available.en_US
dc.identifier.citationFoldvik Eikeland OF, Bianchi FM, Chiesa M, Apostoleris H, Hansen M, Chiou Y. Predicting Energy Demand in Semi-Remote Arctic Locations. Energies. 2021;798en_US
dc.identifier.cristinIDFRIDAID 1888605
dc.identifier.doihttps://doi.org/10.3390/en14040798
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/10037/21823
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofEikeland, O.F. (2023). Enhancing Decision-making in the Electric Power Sector with Machine Learning and Optimization. (Doctoral thesis). <a href=https://hdl.handle.net/10037/31514>https://hdl.handle.net/10037/31514</a>.
dc.relation.journalEnergies
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titlePredicting Energy Demand in Semi-Remote Arctic Locationsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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