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Title: Energy efficiency modeling and predicting using advanced machine learning
Authors: Tan, Si Heng
Keywords: Engineering::Civil engineering::Construction management
Engineering::Computer science and engineering::Software::Programming techniques
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Tan, S. H. (2021). Energy efficiency modeling and predicting using advanced machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: In 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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:CEE Student Reports (FYP/IA/PA/PI)

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