Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150353
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dc.contributor.authorTan, Si Hengen_US
dc.date.accessioned2021-05-25T08:37:26Z-
dc.date.available2021-05-25T08:37:26Z-
dc.date.issued2021-
dc.identifier.citationTan, S. H. (2021). Energy efficiency modeling and predicting using advanced machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150353en_US
dc.identifier.urihttps://hdl.handle.net/10356/150353-
dc.description.abstractIn order to push for further energy conservation and greenhouse emission reduction, a hybrid clustering-based prediction approach is proposed to estimate building energy performance. Our proposed method will be examined through the use of a case study, which involves a dataset containing Chicago’s building energy performance. The reported data is collected by the government, with the aim to tracking the cardon dioxide consumption and building energy efficiency. The dataset is first pre-processed through data cleansing and simplification. By combining the density-based spatial clustering of applications with noise (DBSCAN) method with the random forest (RF) method, regression analysis is used to predict the consumption and efficiency in different clusters. This research aims to combine unsupervised and supervised learning methods to predict building energy consumption with increased accuracy.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Civil engineering::Construction managementen_US
dc.subjectEngineering::Computer science and engineering::Software::Programming techniquesen_US
dc.titleEnergy efficiency modeling and predicting using advanced machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorZhang Limaoen_US
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.description.degreeBachelor of Engineering (Civil)en_US
dc.contributor.supervisoremaillimao.zhang@ntu.edu.sgen_US
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Appears in Collections:CEE Student Reports (FYP/IA/PA/PI)
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