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dc.contributor.authorWard, Rebeccaen
dc.contributor.authorWong, Cheryl Sze Yinen
dc.contributor.authorChong, Adrianen
dc.contributor.authorChoudhary, Ruchien
dc.contributor.authorRamasamy, Savithaen
dc.date.accessioned2021-02-13T00:30:37Z
dc.date.available2021-02-13T00:30:37Z
dc.date.issued2021-04-15en
dc.identifier.issn0378-7788
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317588
dc.description.abstractSignificant reduction in energy demand from non-domestic buildings is required if greenhouse emission reduction targets are to be met worldwide. Increasing monitoring of electricity consumption generates a real opportunity for gaining an in-depth understanding of the nature of occupant-related internal loads and the connection between activity and demand. The stochastic nature of the demand is well-known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. This paper presents evidence that it is feasible to generate stochastic models of activity-related electricity demand based on monitored data. Two machine learning approaches are used to develop stochastic models of plug loads; an autoencoder (AE) and a functional data analysis (FDA) model. Using data from two office buildings located in different countries, the transferability of models is explored by training the models on data from one building and using the trained models to predict demand for the other building. The results show that both models predict plug loads satisfactorily, with a good agreement with the mean demand and quantification of the uncertainty.
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectricity demanden
dc.subjectPlug loadsen
dc.subjectStochastic modelen
dc.subjectMachine learningen
dc.subjectTransferabilityen
dc.subjectAutoencoder (AE)en
dc.subjectFunctional Data Analysis (FDA)en
dc.titleA study on the transferability of computational models of building electricity load patterns across climatic zonesen
dc.typeArticle
prism.numberARTN 110826en
prism.publicationDate2021en
prism.publicationNameENERGY AND BUILDINGSen
prism.volume237en
dc.identifier.doi10.17863/CAM.64701
dcterms.dateAccepted2021-02-02en
rioxxterms.versionofrecord10.1016/j.enbuild.2021.110826en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2021-04-15en
dc.contributor.orcidWard, Rebecca [0000-0001-9384-1957]
dc.identifier.eissn1872-6178
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (1492758)
pubs.funder-project-idEPSRC (EP/F034350/1)
pubs.funder-project-idEPSRC (EP/I019308/1)
pubs.funder-project-idEPSRC (EP/K000314/1)
pubs.funder-project-idEPSRC (EP/L010917/1)
pubs.funder-project-idEPSRC (EP/L024454/1)
pubs.funder-project-idEPSRC (EP/N021614/1)
cam.orpheus.successMon Aug 16 07:32:28 BST 2021 - Embargo updated*
cam.orpheus.counter26*
rioxxterms.freetoread.startdate2022-04-15


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International