Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction

DC FieldValueLanguage
dc.contributor.authorAli, Usman-
dc.contributor.authorShamsi, Mohammad Haris-
dc.contributor.authorAlshehri, Fawaz-
dc.contributor.authorMangina, Eleni-
dc.contributor.authorO'Donnell, James-
dc.date.accessioned2021-06-21T11:27:28Z-
dc.date.available2021-06-21T11:27:28Z-
dc.date.copyright2019 the Authorsen_US
dc.date.issued2019-09-04-
dc.identifier.isbn9781775052012-
dc.identifier.issn2522-2708-
dc.identifier.urihttp://hdl.handle.net/10197/12263-
dc.descriptionThe 16th International Building Simulation Association, Rome, Italy, 2-4 September 2019en_US
dc.description.abstractEnergy Performance Certificates (EPC) provide an indication of buildings’ energy use. The creation of an EPC for individual building requires information surveys. Hence, these ratings are typically non-existent for entire building stock. This paper addresses these information gaps using machine-learning models. Developed models were evaluated with Irish EPC data that included approximately 650,000 residential buildings with 199 inputs variables. Results indicate that the deep learning algorithm produces results with highest accuracy level of 88% when only 82 input variables are available. This identified approach will allow stakeholders such as authorities, policy makers and urban-planners to determine the EPC rating for the rest of the building stock using limited data.en_US
dc.description.sponsorshipUniversity College Dublinen_US
dc.language.isoenen_US
dc.publisherIBPSAen_US
dc.relation.ispartofCorrado, V. F,abrizio, E. Gasparella, A. and Patuzzi, F. (eds.). Building Simulation 2019en_US
dc.subjectBuilding energy performanceen_US
dc.subjectPredictionen_US
dc.subjectMachine learning algorithmsen_US
dc.titleApplication Of Intelligent Algorithms For Residential Building Energy Performance Rating Predictionen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactotherjames.odonnell@ucd.ieen_US
dc.internal.webversionshttp://buildingsimulation2019.org/-
dc.statusPeer revieweden_US
dc.identifier.doi10.26868/25222708.2019.210232-
dc.neeo.contributorAli|Usman|aut|-
dc.neeo.contributorShamsi|Mohammad Haris|aut|-
dc.neeo.contributorAlshehri|Fawaz|aut|-
dc.neeo.contributorMangina|Eleni|aut|-
dc.neeo.contributorO'Donnell|James|aut|-
dc.description.othersponsorshipESIPP UCDen_US
dc.date.updated2020-08-12T15:11:49Z-
dc.identifier.grantid15/spp/e3125-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Energy Institute Research Collection
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