A preference-based multi-objective building performance optimization method for early design stage

Author(s):  
Borong Lin ◽  
Hongzhong Chen ◽  
Yanchen Liu ◽  
Qiushi He ◽  
Ziwei Li
2018 ◽  
Vol 11 (4) ◽  
pp. 647-661 ◽  
Author(s):  
Ziwei Li ◽  
Hongzhong Chen ◽  
Borong Lin ◽  
Yingxin Zhu

BORDER ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 53-64
Author(s):  
Fenty Ratna Indarti

Due to the ozone layer depletion, global warming and climate change, there is a significant increase to reduce carbon emission. Practitioners and academia undertake studies to promote environmentally friendly built environments. Developed countries have established specific standards to achieve a carbon neutral as their commitment to contribute for a better earth condition. Design phases are considered as the early stage where the environmental approach needs to be applied to predict the building performance as soon as possible to maximise the energy efficiency of the proposed building. Another significant factor affecting the building energy performance is climate. Climate becomes the first parameter to generate building proposals as it is contextual to the site. This study aims to assess the application of environmental approach in designing educational building in temperate climate during the early design stage. The combination of design and simulation during the early design stage, helps to define the best design proposal to adopt passive design that harvest the environment condition as much as possible to deliver comfort into the building.


2021 ◽  
Vol 9 ◽  
Author(s):  
Longwei Zhang ◽  
Chao Wang ◽  
Yu Chen ◽  
Lingling Zhang

Large-space buildings feature a sizable interface for receiving solar radiation, and optimizing their shape in the early design stage can effectively increase their solar energy harvest while considering both energy efficiency and space utilization. A large-space building shape optimization method was developed based on the “modeling-calculation-optimization” process to transform the “black box” mode in traditional design into a “white box” mode. First, a two-level node control system containing core space variables and envelope variables is employed to construct a parametric model of the shape of a large-space building. Second, three key indicators, i.e., annual solar radiation, surface coefficient, and space efficiency, are used to representatively quantify the performance in terms of sunlight capture, energy efficiency, and space utilization. Finally, a multi-objective genetic algorithm is applied to iteratively optimize the building shape, and the Pareto Frontier formed by the optimization results provides the designer with sufficient alternatives and can be used to assess the performance of different shapes. Further comparative analysis of the optimization results can reveal the typical shape characteristics of the optimized solutions and potentially determine the key variables affecting building performance. In a case study of six large-space buildings with typical shapes, the solar radiation of the optimized building shape solutions was 13.58–39.74% higher than that of reference buildings 1 and 3; compared with reference buildings 2 and 4, the optimized solutions also achieved an optimal balance of the three key indicators. The results show that the optimization method can effectively improve the comprehensive performance of buildings.


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