Dual-objective optimization for cost and carbon emissions of green residential buildings based on SVM-NSGA-II coupling

DOI

10.2991/978-94-6463-316-0_21How to use a DOI?

Keywords

Green residential; cost; carbon emissions; optimization research

Abstract

In order to further reduce the life cycle cost and carbon emissions of green residential buildings, this paper proposes a dual-objective optimization framework based on the coupling of Support Vector Machine (SVM) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework simulates the design process and calculates the corresponding life cycle cost and carbon emissions from a design perspective. By using the SVM-NSGA-II algorithm, optimal solutions for green residential building designs are obtained. This approach is then applied to a green building project in Beijing, aiming to achieve a win-win situation in terms of economic and environmental benefits.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bing Xu
AU  - Meng Zhao
PY  - 2023
DA  - 2023/12/14
TI  - Dual-objective optimization for cost and carbon emissions of green residential buildings based on SVM-NSGA-II coupling
BT  - Proceedings of the 2023 5th International Conference on Civil Engineering, Environment Resources and Energy Materials (CCESEM 2023)
PB  - Atlantis Press
SP  - 180
EP  - 187
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-316-0_21
DO  - 10.2991/978-94-6463-316-0_21
ID  - Xu2023
ER  -