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Dec 15, 2022
Research Status of Bottom-Up Building Energy Consumption Modeling Based on Bibliometrics
Authors: Yuxin Fan [email protected], Zhaoyao Jiang [email protected], and Kailun Feng [email protected]Author Affiliations
Publication: ICCREM 2022
ABSTRACT
Energy consumption and greenhouse gas significantly contribute to the global warming. Building-related energy consumption accounts for 40% of the world’s total energy consumption. It is necessary to use computer-aided technology such as BIM-based performance simulation to build energy consumption modeling and analyze the energy consumption of buildings in different building scales. The approach of building energy consumption modeling can be divided into top-down and bottom-up. The bottom-up approach firstly get dispersed buildings’ energy consumption data, then sum up dispersed data, and simulate the building energy consumption of required building scales (e.g., urban, national). In this paper, VOSviewer is used to perform a bibliometric analysis on the literature from Web of Science. The development prospects and obstacles of bottom-up approach are analyzed, considering the Chinese construction industry energy saving demand and technology application status in China.
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REFERENCES
Abbasabadi, N., Ashayeri, M., and Azari, R. (2019). “An integrated data-driven framework for urban energy use modeling (UEUM).” Applied Energy, 253.
Kwok, Y. T., Schoetter, R., and Ng, E. (2022). “Towards decarbonisation targets by changing setpoint temperature to avoid building overcooling and implementing district cooling in (sub) tropical high-density cities: A case study of Hong Kong.” Science of the Total Environment, 811, 152338.
Leng, H., Sun, Y., and Bai, J. (2015). “A Review of research on development and application of city building energy consumption models.” Architectural Journal, 2015(S1), 221–227. (in Chinese).
Li, Y. X., Wu, Y., Wang, L., Wang, C., and Shi, X. (2020). “Comparative study on simulation methods of urban energy consumption.” Urban Planning International, 35(02), 80–86. (in Chinese).
Thrampoulidis, E., Mavromatidis, G., Lucchi, A., and Orehounig, K. (2021). “A machine learning-based surrogate model to approximate optimal building retrofit solutions.” Applied Energy, 281, 116024.
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Published online: Dec 15, 2022
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1Postgraduate, School of Civil Engineering, Harbin Institute of Technology, Harbin, China. Email: [email protected]
2Senior Engineer, China Construction Industry Association, Beijing, China. Email: [email protected]
3Assistant Professor, School of Civil Engineering, Harbin Institute of Technology, Harbin, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.