首页> 外文OA文献 >Building Energy Modeling for Green Architecture and Intelligent Dashboard Applications
【2h】

Building Energy Modeling for Green Architecture and Intelligent Dashboard Applications

机译:用于绿色建筑和智能仪表板应用的建筑能源建模

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Buildings are responsible for 40% of the carbon emissions in the United States. Energy efficiency in this sector is key to reducing overall greenhouse gas emissions. This work studied the passive technique called the roof solar chimney for reducing the cooling load in homes architecturally. Three models of the chimney were created: a zonal building energy model, computational fluid dynamics model, and numerical analytic model. The study estimated the error introduced to the building energy model (BEM) through key assumptions, and then used a sensitivity analysis to examine the impact on the model outputs. The conclusion was that the error in the building energy model is small enough to use it for building simulation reliably. Further studies simulated the roof solar chimney in a whole building, integrated into one side of the roof. Comparisons were made between high and low efficiency constructions, and three ventilation strategies. The results showed that in four US climates, the roof solar chimney results in significant cooling load energy savings of up to 90%. After developing this new method for the small scale representation of a passive architecture technique in BEM, the study expanded the scope to address a fundamental issue in modeling - the implementation of the uncertainty from and improvement of occupant behavior. This is believed to be one of the weakest links in both accurate modeling and proper, energy efficient building operation. A calibrated model of the Mascaro Center for Sustainable Innovation’s LEED Gold, 3,400 m2 building was created. Then algorithms were developed for integration to the building’s dashboard application that show the occupant the energy savings for a variety of behaviors in real time. An approach using neural networks to act on real-time building automation system data was found to be the most accurate and efficient way to predict the current energy savings for each scenario. A stochastic study examined the impact of the representation of unpredictable occupancy patterns on model results. Combined, these studies inform modelers and researchers on frameworks for simulating holistically designed architecture and improving the interaction between models and building occupants, in residential and commercial settings.
机译:在美国,建筑物的碳排放量占40%。该部门的能源效率是减少整体温室气体排放的关键。这项工作研究了一种称为屋顶太阳能烟囱的被动技术,以减少建筑上房屋的制冷负荷。创建了三个烟囱模型:区域建筑能耗模型,计算流体动力学模型和数值分析模型。该研究通过关键假设估计了引入建筑能耗模型(BEM)的误差,然后使用敏感性分析来检查对模型输出的影响。结论是建筑能量模型中的误差足够小,可以可靠地用于建筑仿真。进一步的研究模拟了整个建筑物中集成到屋顶一侧的屋顶太阳能烟囱。比较了高效率和低效率结构以及三种通风策略。结果表明,在美国的四个气候中,屋顶太阳能烟囱可显着节省高达90%的制冷负荷。在为BEM中的被动式建筑技术的小规模表示开发了这种新方法之后,研究扩大了范围,以解决建模中的一个基本问题-实施不确定性和改善乘员行为。在准确的建模和适当的节能建筑操作中,这被认为是最薄弱的环节之一。建立了Mascaro可持续创新中心的LEED金牌3400平方米建筑的校准模型。然后,开发了算法,以将其集成到建筑物的仪表板应用程序中,从而向居住者实时显示各种行为的节能量。发现使用神经网络对实时楼宇自动化系统数据进行操作的方法是预测每种方案当前节能量的最准确,最有效的方法。一项随机研究检查了不可预测的占用模式表示对模型结果的影响。综合起来,这些研究为建模人员和研究人员提供了有关在住宅和商业环境中模拟整体设计架构并改善模型与建筑居民之间相互作用的框架的信息。

著录项

  • 作者

    DeBlois Justin C.;

  • 作者单位
  • 年度 2014
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
  • 中图分类

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号