Improving the performance of prefabricated houses through multi-objective optimization design

被引:19
作者
Ji, Yingbo [1 ]
Lv, Junyi [2 ]
Li, Hong Xian [3 ]
Liu, Yan [1 ]
Yao, Fuyi [1 ]
Liu, Xinnan [1 ]
Wang, Siqi [1 ]
机构
[1] North China Univ Technol, Sch Civil Engn, Beijing 100144, Peoples R China
[2] Zibo Vocat Inst, Zibo 255314, Peoples R China
[3] Deakin Univ, Sch Architecture & Built Environm, Geelong, Vic 3220, Australia
关键词
Prefabricated house; Energy simulation; Multi -objective optimization; Life -cycle costing; Life -cycle CO 2 emissions; BUILDING ENERGY-CONSUMPTION; ARTIFICIAL NEURAL-NETWORKS; GENETIC ALGORITHM; THERMAL COMFORT; ENVELOPE DESIGN; SIMULATION; COST; RETROFIT; MODEL; RENOVATION;
D O I
10.1016/j.jobe.2024.108579
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Building prefabrication technology provides opportunities to improve the efficiency of construction process; however, there is a missing link between building prefabrication and sustainable building design. To achieve balanced energy efficiency, economic performance, and environmental objectives of prefabricated houses, this research proposes a multi-objective optimization framework to minimize building energy consumption, costing, and carbon emission. Firstly, based on the selected 16 design parameters, the energy performance of 1.89 x 1013 design solutions is simulated using a BIM model, DesignBuilder, and JEPlus coupled with a developed Excel program. Then, multi-objective optimization is conducted to optimize comprehensive building energy consumption (CVBEC), life-cycle costing (LCC), and life-cycle carbon emission (LCCO2), with Artificial Neural Network (ANN) coupled with NSGA-II algorithm used to achieve the Pareto optimal solutions. The proposed framework is demonstrated in a prefabricated steel house in Beijing. Results show that the design solution with the smallest CVBEC, LCC, and LCCO2 among the Pareto optimal solutions can reduce the CVBEC by 127.4 %-117.9 %, LCC by 20.3 %- 4.5 %, and LCCO2 by 150.9 %-145.5 %. The PV system sizing is then considered for further analysis. Compared to the three Pareto optimal solutions without PV, the solution with an 8 kW PV system results in a reduction in CVBEC by 87.6 kWh/m2, LCC by 84.4 CNY/m2, and LCCO2 reduction of 1121.7 kgCO2eq/m2. This research customizes an optimization framework for prefabricated houses, which can be quickly solved to obtain the optimal energy-efficient design solutions for prefabricated houses in terms of energy efficiency, economic performance, and environmental performance, and allows designers to know how they should choose prefabricated enclosure, building orientation, and photovoltaic (PV) system, etc., and therefore can be effectively used for energy-efficient design of prefabricated houses.
引用
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页数:20
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