Paper Title :Data-Driven Urban Energy Simulation for Mega-City by Integrating Machine Learning Into an Urban Building Energy Simulation Modeling: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area
Author :Hsi-Hsien Wei
Article Citation :Hsi-Hsien Wei ,
(2023 ) " Data-Driven Urban Energy Simulation for Mega-City by Integrating Machine Learning Into an Urban Building Energy Simulation Modeling: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area " ,
International Journal of Advances in Mechanical and Civil Engineering (IJAMCE) ,
pp. 7-9,
Volume-10,Issue-4
Abstract : Understanding regional building energy patterns is the prerequisite to efficiently and effectively promote
sustainable urban development. Previous studies have proposed various data-driven methods to investigate the
relationship between building energy consumption and hundreds of potential influencing features. To identify the critical
features, this study develops a data-driven random forest (RF) based framework, consisting of 24,764 buildings in 881 cityblocks,
to model the relationship between city-block-level building-oriented features and building energy consumption. The
RF model is found to outperform other machine learning models including logistic regression, k-nearest neighborhood,
support vector machine, and decision tree models in the predictive accuracy of the classification problem.
Keyword - Building energy modeling
Type : Research paper
Published : Volume-10,Issue-4
DOIONLINE NO - IJAMCE-IRAJ-DOIONLINE-19998
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Copyright: © Institute of Research and Journals
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Published on 2023-11-11 |
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