DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Mechanical and Civil Engineering (IJAMCE)-IJAMCE |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-4 ( Aug, 2023 ) | |||||||||
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 Name |
Hsi-Hsien Wei | |||||||||
Affilition |
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Pages |
7-9 | |||||||||
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 | |||||||||
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