基于BIM技术的被动式建筑节能因子多目标优化研究

Research on multi-objective optimization of passive building energy-saving factor based on BIM

为了研究被动式建筑节能策略,在原有的BIM模型基础上生成建筑能耗模型,通过gbXML数据标准进行数据共享,在Grasshopper平台导入参数化建筑性能模拟模型,对目标建筑外表面进行太阳辐射分析,确定以西面遮阳板倾斜角和深度、南面和西面窗墙比、外墙保温板厚度为被动式节能技术变量指标。利用OpenStudio进行建筑能耗分析,Daysim进行全年动态自然采光模拟分析,以空间日光自主评价指标sDA300/50%、全年制冷、供暖能耗为相互制衡的适应度目标函数。最后使用NSGA-Ⅱ算法进行多目标优化,得出帕累托前沿解集。研究表明:寒冷B区,固定遮阳无法平衡制冷和供暖能耗目标。窗墙比仅通过制冷、供暖能耗目标无法进行优化设计,应结合自然采光性能进行制衡。同时增加保温层厚度,提升外墙保温效果。BIM模型提供了建筑性能模拟数据来源,Grasshopper平台结合模拟引擎和优化算法进行耦合分析,为被动式节能因子指标最优值的搜索带来新的思路。

Building energy models on the basis of the original building information modeling(BIM) model were established to research the passive energy-saving strategies. This model adopted the gbXML data standard for data sharing, which imported a parametric building performance simulation model on the Grasshopper platform. In addition, the solar radiation analysis was performed on the outer surface of the target building to determine the variables index of the passive energy-saving technology, such as the angle and depth of the west overhangs, the window-to-wall ratio on the south and west sides, and the thickness of the insulation board. This platform established the objective function based on the spatial daylight autonomy(sDA300/50%), annual cooling and heating energy consumption, which employed OpenStudio for building energy analysis and Daysim for annual dynamic natural lighting analysis. Finally, using the NSGA-Ⅱ algorithm for multi-objective optimization, the Pareto solution set was obtained. The result shows that on cold zone B, the fixed shading cannot balance cooling and heating energy consumption. The window-to-wall ratio cannot be optimized only through cooling and heating energy consumption, which should combine natural lighting performance. At the same time, it should increase the thickness of the insulation layer to enhance the insulation effect of the external wall. The BIM model provides a source of building performance simulation data, and the Grasshopper platform combines simulation engines and optimization algorithms for coupling analysis, bringing new ideas to the search of optimal values of passive energy-saving factor index.