Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.enbuild.2004.09.009
DC FieldValue
dc.titleApplying support vector machines to predict building energy consumption in tropical region
dc.contributor.authorDong, B.
dc.contributor.authorCao, C.
dc.contributor.authorLee, S.E.
dc.date.accessioned2014-12-01T08:23:17Z
dc.date.available2014-12-01T08:23:17Z
dc.date.issued2005-05
dc.identifier.citationDong, B., Cao, C., Lee, S.E. (2005-05). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37 (5) : 545-553. ScholarBank@NUS Repository. https://doi.org/10.1016/j.enbuild.2004.09.009
dc.identifier.issn03787788
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/113973
dc.description.abstractThe methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature (T0), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and ε, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%. © 2004 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.enbuild.2004.09.009
dc.sourceScopus
dc.subjectBuilding energy consumption prediction
dc.subjectSupport vector machine
dc.subjectTropical region
dc.subjectWeather data
dc.typeArticle
dc.contributor.departmentBUILDING
dc.description.doi10.1016/j.enbuild.2004.09.009
dc.description.sourcetitleEnergy and Buildings
dc.description.volume37
dc.description.issue5
dc.description.page545-553
dc.description.codenENEBD
dc.identifier.isiut000227366000012
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