A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance

Yingni Zhai, Yi Wang, Yanqiu Huang and Xiaojing Meng

Renewable Energy, 2019, vol. 134, issue C, 1190-1199

Abstract: Window design involves various parameters such as the orientation, window size, and glass material. These parameters have a significant, interactive influence on the building performance. Therefore, it is important to simultaneously optimize the window parameters to determine trade-off design solutions between energy consumption, indoor thermal environment and visual performance. In this paper, a multi-objective optimization method that combines the Non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II) with EnergyPlus is proposed for window design optimization. The method takes many parameters into consideration and optimizes several objectives to assess their overall performance. It is applied to an office room with various window parameters and three building design objectives. The Pareto approach is used to select optimal solutions. All Pareto-optimal solutions are shown in the Pareto-frontier charts, which clearly illustrate the performance of each solution. A preliminary analysis of Pareto-optimal solutions was performed to illustrate the value distribution of the window parameters for each orientation. The method provides the architects rich and valuable information about the effects of the parameters on the different building design objectives. It can help the designers to obtain an optimal window design solution to minimize the building energy consumption while simultaneously improving the indoor thermal environment and visual performance.

Keywords: Multi-objective optimization; Window design; NSGA-II; Energy consumption; Thermal environment; Visual performance (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:134:y:2019:i:c:p:1190-1199

DOI: 10.1016/j.renene.2018.09.024

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