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图学学报 ›› 2021, Vol. 42 ›› Issue (1): 124-132.DOI: 10.11996/JG.j.2095-302X.2021010124

• 建筑与城市信息模型 • 上一篇    下一篇

基于 BIM 技术的被动式建筑节能因子 多目标优化研究

  

  1. 华北水利水电大学水利学院,河南 郑州 450000
  • 出版日期:2021-02-28 发布日期:2021-02-01
  • 基金资助:
    国家自然科学基金项目(51709115);河南省重点研发与推广专项(科技攻关)项目(182102210066) 

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

  1. School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou Henan 450000, China
  • Online:2021-02-28 Published:2021-02-01
  • Supported by:
    The National Natural Science Foundation of China (51709115); Henan Province Key R&D and Promotion Special (Technology Research) Project (182102210066) 

摘要: 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。

关键词: 建筑信息模型, 被动式建筑, 节能因子, 建筑性能分析, 多目标优化

Abstract: 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 

Key words: building information modeling, passive building, energy-saving factor, building performance analysis, multi-objective optimization

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