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dc.contributor.authorWard, Rebecca
dc.contributor.authorWong, Cheryl Sze Yin
dc.contributor.authorChong, Adrian
dc.contributor.authorChoudhary, Ruchi
dc.contributor.authorRamasamy, Savitha
dc.date.accessioned2021-02-13T00:30:37Z
dc.date.available2021-02-13T00:30:37Z
dc.date.issued2021
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 BV
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectricity demand
dc.subjectPlug loads
dc.subjectStochastic model
dc.subjectMachine learning
dc.subjectTransferability
dc.subjectAutoencoder (AE)
dc.subjectFunctional Data Analysis (FDA)
dc.titleA study on the transferability of computational models of building electricity load patterns across climatic zones
dc.typeArticle
prism.numberARTN 110826
prism.publicationDate2021
prism.publicationNameENERGY AND BUILDINGS
prism.volume237
dc.identifier.doi10.17863/CAM.64701
dcterms.dateAccepted2021-02-02
rioxxterms.versionofrecord10.1016/j.enbuild.2021.110826
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-04-15
dc.contributor.orcidWard, Rebecca [0000-0001-9384-1957]
dc.identifier.eissn1872-6178
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (1492758)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/F034350/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/I019308/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K000314/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L010917/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L024454/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (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