Application of Artificial Neural Networks for Virtual Energy Assessment

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dc.contributor.authorMortazavigazar, Amiren
dc.contributor.authorWahba, Nourehanen
dc.contributor.authorNewsham, Paulen
dc.contributor.authorTriharta, Mahartien
dc.contributor.authorZheng, Pufanen
dc.contributor.authorChen, Tracyen
dc.contributor.authorRismanchi, Behzaden
dc.date.accessioned2021-12-11T17:09:35Zen
dc.date.available2021-12-11T17:09:35Zen
dc.date.updated2021-12-11T17:09:28Zen
dc.description.abstractA Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.en
dc.description.versionPublished versionen
dc.format.extentPages 8330-8330en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3390/en14248330en
dc.identifier.eissn1996-1073en
dc.identifier.issue24en
dc.identifier.orcidMortazavigazar, Amir [0000-0002-8962-4279]en
dc.identifier.urihttp://hdl.handle.net/10919/106941en
dc.identifier.volume14en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subject02 Physical Sciencesen
dc.subject09 Engineeringen
dc.titleApplication of Artificial Neural Networks for Virtual Energy Assessmenten
dc.title.serialEnergiesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Veterinary Medicineen
pubs.organisational-group/Virginia Tech/Veterinary Medicine/Biomedical Sciences and Pathobiologyen
pubs.organisational-group/Virginia Tech/Graduate studentsen
pubs.organisational-group/Virginia Tech/Graduate students/Doctoral studentsen

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