【摘 要】
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With the development of the economic and low-carbon society, high-performance building ( HPB) design plays an increasingly important role in the architectural area. The performance of buildings usually includes the building energy consumption, building in
【机 构】
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Heilongjiang Cold Region Architectural Science Key Laboratory,School of Architecture,Harbin Institut
论文部分内容阅读
With the development of the economic and low-carbon society, high-performance building ( HPB) design plays an increasingly important role in the architectural area. The performance of buildings usually includes the building energy consumption, building interior natural daylighting, building surface solar radiation, and so on. Building performance simulation ( BPS) and multiple objective optimizations ( MOO) are becoming the main methods for obtaining a high performance building in the design process. Correspondingly, the BPS and MOO are based on the parametric tools, like Grasshopper and Dynamo. However, these tools are lacking the data analysis module for designers to select the high-performance building more conveniently. This paper proposes a toolkit“GPPre”developed based on the Grasshopper platform and Python language. At the end of this paper, a case study was conducted to verify the function of GPPre, which shows that the combination of the sensitivity analysis ( SA) and MOO module in the GPPre could aid architects to design the buildings with better performance.
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