Abstract
Because of the large number of buildings and the complexity of influencing factors in urban building energy analysis, the computational cost of large-scale energy models for urban buildings is high. Hence, this paper implements a machine learning-based approach to assess carbon emissions of urban buildings. A campus in China is used as a case study to demonstrate this approach, including four steps: automation of construction of engineering-based energy models in a geographic information system (GIS) environment, construction of a matrix of input and output data, creating machine learning models, and sensitivity and carbon emission analysis of urban buildings. The results indicate that this method can effectively assess carbon emissions for urban buildings with different energy-saving measures based on machine learning energy models. The accuracy of machine learning models should be carefully evaluated before implementing them in carbon emission analysis for urban buildings.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 51778416) and the Key Projects of Philosophy and Social Sciences Research, Ministry of Education (China) (contract No. 16JZDH014).
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Liu, Y., Tian, W., Zhou, X. (2020). Carbon Performance Evaluation of Urban Buildings Using Machine Learning-Based Energy Models. In: Wang, Z., Zhu, Y., Wang, F., Wang, P., Shen, C., Liu, J. (eds) Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019). ISHVAC 2019. Environmental Science and Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-13-9528-4_139
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