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Research and Accuracy Analysis on Engineering Quantity Calculation of a Projected Building by BIM Software Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Carbon Performance Evaluation of Urban Buildings Using Machine Learning-Based Energy Models

Authors : Yunliang Liu, Wei Tian, Xiang Zhou

Published in: Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Publisher: Springer Singapore

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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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previous chapter Evaluation of Radiant Heating and Cooling Terminals Based on Structural Thermal Resistance
next chapter Study on the Degree and Mechanism of Solar Shading Impacts from Rooftop Structures in the Solar Collector Layout Area
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Metadata
Title
Carbon Performance Evaluation of Urban Buildings Using Machine Learning-Based Energy Models
Authors
Yunliang Liu
Wei Tian
Xiang Zhou
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Print ISBN: 978-981-13-9527-7

Electronic ISBN: 978-981-13-9528-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-9528-4_139