Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2019
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| _version_ | 1866911068166553600 |
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| author | Yu, Zhongqi Chen, Shisheng Wong, Nyuk Hien Ignatius, Marcel Deng, Jiyu He, Yueer Hii, Daniel Jun Chung |
| author_facet | Yu, Zhongqi Chen, Shisheng Wong, Nyuk Hien Ignatius, Marcel Deng, Jiyu He, Yueer Hii, Daniel Jun Chung |
| contents | The rising air temperature caused by Urban Heat Island (UHI) effect has become a problem for Singapore, it not only affects the thermal comfort of outdoor microclimate environment, but also increases the cooling energy consumption of buildings. As part of a multiscale and multi-physics urban microclimate model, weather stations were installed at 15 points within kent ridge campus of National University of Singapore (NUS) and continuously recorded the microclimate data from February 2019 to May 2019. A Geographical Information System (GIS) map and 3D model were constructed for extracting urban morphology parameters such as BDG, PAVE, WALL and HBDG. Through a site survey, SVF and GnPR were calculated. By using multi-criteria linear regression and machine learning, this research investigated five regression models for prediction of outdoor air temperature including linear regression (LR), k-nearest neighbours (KNN), support vector regression (SVR), decision tree (DT) and random forests (RF). The analysis of variables by best subsets regression showed greenery played crucial role in the mitigation of both daytime and night-time UHI. Pedestrian level wind flow was helpful in heat release in the daytime. High-rise buildings provided self-shadowing to reduce ambient air temperature but higher SVF was harmful to heat release in the night-time. For regression models, RF had the best predictive performance. Average RMSE of RF was reduced by 4% to 29% compared to linear regression. The learning curve indicated that the predictive power of LR could not be improved by additional data provision. In contrast, the downward trend in bias and variance suggested that RF can benefit from the training of big data. During the deployment of learning algorithms, RF continued to outperform other learning algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1911_08822 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore Yu, Zhongqi Chen, Shisheng Wong, Nyuk Hien Ignatius, Marcel Deng, Jiyu He, Yueer Hii, Daniel Jun Chung Physics and Society Applications The rising air temperature caused by Urban Heat Island (UHI) effect has become a problem for Singapore, it not only affects the thermal comfort of outdoor microclimate environment, but also increases the cooling energy consumption of buildings. As part of a multiscale and multi-physics urban microclimate model, weather stations were installed at 15 points within kent ridge campus of National University of Singapore (NUS) and continuously recorded the microclimate data from February 2019 to May 2019. A Geographical Information System (GIS) map and 3D model were constructed for extracting urban morphology parameters such as BDG, PAVE, WALL and HBDG. Through a site survey, SVF and GnPR were calculated. By using multi-criteria linear regression and machine learning, this research investigated five regression models for prediction of outdoor air temperature including linear regression (LR), k-nearest neighbours (KNN), support vector regression (SVR), decision tree (DT) and random forests (RF). The analysis of variables by best subsets regression showed greenery played crucial role in the mitigation of both daytime and night-time UHI. Pedestrian level wind flow was helpful in heat release in the daytime. High-rise buildings provided self-shadowing to reduce ambient air temperature but higher SVF was harmful to heat release in the night-time. For regression models, RF had the best predictive performance. Average RMSE of RF was reduced by 4% to 29% compared to linear regression. The learning curve indicated that the predictive power of LR could not be improved by additional data provision. In contrast, the downward trend in bias and variance suggested that RF can benefit from the training of big data. During the deployment of learning algorithms, RF continued to outperform other learning algorithms. |
| title | Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore |
| topic | Physics and Society Applications |
| url | https://arxiv.org/abs/1911.08822 |