Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Multi-objective Optimization Approach of Green Building Performance Based on LGBM and AGE-MOEA
https://orcid.org/http://orcid.org/0000-0003-1935-7387
https://orcid.org/http://orcid.org/0000-0002-0521-250X
This paper proposes a novel multi-objective optimization approach for green building performance combining Building Information Model (BIM) and artificial intelligence (AI) algorithms. LGBM and AGE-MOEA algorithm are respectively utilized to construct the prediction and optimization model, and finally the multi-objective optimization process for total site energy consumption, CO2 emission and discomfort hours can be achieved by changing values of 12 corresponding level-2 indicators. The pareto front and the optimal solution are obtained. The applicability and effectiveness of the optimization method are verified by a practical case, and the results suggest that: (1) the accuracy of LGBM prediction model is as high as 99.975%; (2) under the multi-objective optimization of AGE-MOEA algorithm, the green performance metrics of building information model has improved by 14.32%, which is better than that of widely used NSGA-II and NSGA-III algorithms under the same conditions; (3) the setting of HAVC system is the biggest factor affecting the performance of green buildings.
A Multi-objective Optimization Approach of Green Building Performance Based on LGBM and AGE-MOEA
https://orcid.org/http://orcid.org/0000-0003-1935-7387
https://orcid.org/http://orcid.org/0000-0002-0521-250X
This paper proposes a novel multi-objective optimization approach for green building performance combining Building Information Model (BIM) and artificial intelligence (AI) algorithms. LGBM and AGE-MOEA algorithm are respectively utilized to construct the prediction and optimization model, and finally the multi-objective optimization process for total site energy consumption, CO2 emission and discomfort hours can be achieved by changing values of 12 corresponding level-2 indicators. The pareto front and the optimal solution are obtained. The applicability and effectiveness of the optimization method are verified by a practical case, and the results suggest that: (1) the accuracy of LGBM prediction model is as high as 99.975%; (2) under the multi-objective optimization of AGE-MOEA algorithm, the green performance metrics of building information model has improved by 14.32%, which is better than that of widely used NSGA-II and NSGA-III algorithms under the same conditions; (3) the setting of HAVC system is the biggest factor affecting the performance of green buildings.
A Multi-objective Optimization Approach of Green Building Performance Based on LGBM and AGE-MOEA
https://orcid.org/http://orcid.org/0000-0003-1935-7387
https://orcid.org/http://orcid.org/0000-0002-0521-250X
This paper proposes a novel multi-objective optimization approach for green building performance combining Building Information Model (BIM) and artificial intelligence (AI) algorithms. LGBM and AGE-MOEA algorithm are respectively utilized to construct the prediction and optimization model, and finally the multi-objective optimization process for total site energy consumption, CO2 emission and discomfort hours can be achieved by changing values of 12 corresponding level-2 indicators. The pareto front and the optimal solution are obtained. The applicability and effectiveness of the optimization method are verified by a practical case, and the results suggest that: (1) the accuracy of LGBM prediction model is as high as 99.975%; (2) under the multi-objective optimization of AGE-MOEA algorithm, the green performance metrics of building information model has improved by 14.32%, which is better than that of widely used NSGA-II and NSGA-III algorithms under the same conditions; (3) the setting of HAVC system is the biggest factor affecting the performance of green buildings.
A Multi-objective Optimization Approach of Green Building Performance Based on LGBM and AGE-MOEA
Lecture Notes in Civil Engineering
Guo, Wei (Herausgeber:in) / Qian, Kai (Herausgeber:in) / Shen, Yuxuan (Autor:in) / Pan, Yue (Autor:in)
International Conference on Green Building, Civil Engineering and Smart City ; 2022 ; Guilin, China
08.09.2022
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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