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dc.contributor.authorNoyé, Sarah
dc.contributor.authorSaralegui, Unai
dc.contributor.authorRey, Raphael
dc.contributor.authorAnton, Miguel Angel
dc.contributor.authorRomero, Ander
dc.date.accessioned2019-09-04T14:03:57Z
dc.date.available2019-09-04T14:03:57Z
dc.date.issued2019
dc.identifier.citationNoyé, Sarah, Unai Saralegui, Raphael Rey, Miguel Angel Anton, and Ander Romero. “Energy Demand Prediction for the Implementation of an Energy Tariff Emulator to Trigger Demand Response in Buildings.” Edited by S.I Tanabe, H. Zhang, J. Kurnitski, M.C. Gameiro da Silva, I. Nastase, P. Wargocki, G. Cao, L. Mazzarela, and C. Inard. E3S Web of Conferences 111 (2019): 05025. doi:10.1051/e3sconf/201911105025.en
dc.identifier.urihttp://hdl.handle.net/11556/755
dc.description.abstractBuildings are key actors of the electrical gird. As such they have an important role to play in grid stabilization, especially in a context where renewable energies are mandated to become an increasingly important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate more efficiently. One of the ways to obtain flexibility from building managers and building users is the introduction of variable energy prices which evolve depending on the expected load and energy generation. In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper, a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random Forest machine learning algorithm.en
dc.description.sponsorshipThis paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768614. This paper reflects only the author´s views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein.en
dc.language.isoengen
dc.publisherEDP Sciencesen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEnergy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildingsen
dc.typearticleen
dc.identifier.doi10.1051/e3sconf/201911105025en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/768614/EU/Integrating Real-Intelligence in Energy Management Systems enabling Holistic Demand Response Optimization in Buildings and Districts/HOLISDERen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsRenewable energiesen
dc.subject.keywordsBuildingen
dc.subject.keywordsVariable energy pricesen
dc.subject.keywordsEnergy tariff emulatoren
dc.subject.keywordsRandom Forest machine learning algorithmen
dc.identifier.essn2267-1242en
dc.journal.titleE3S Web of Conferencesen
dc.page.initial05025en
dc.volume.number111en


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