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Validation of retrofit analysis simulation tool: Lessons learned

机译:改造分析仿真工具的验证:学习的经验教训

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It is well known that residential and commercial buildings account for about 40% of the overall energy consumed in the United States, and about the same percentage of CO_2 emissions. Retrofitting existing old buildings, which account for 99% of the building stock, represents the best opportunity of achieving challenging energy and emission reduction targets. United Technologies Research Center (UTRC) has developed a methodology and tool that provides computational support for analysis and decision-making for building retrofits. The tool is based on simplified physics-based models and incorporates intelligent defaulting capability, automatic model calibration and package selection, as well as uncertainty quantification and sensitivity analysis (UQ/SA) on both predicted energy consumption and potential savings. UQ/SA is used to better inform decision makers on the quality of the data used for analysis and direct them in the overall process to achieve the required accuracy in the analysis. This paper addresses the validation of the simplified physics-based models. The validation is performed using a three-tiered approach: a) validation against ASHRAE 140 BESTEST Cases; b) inter-model comparison of results obtained by other more complex tools using more detailed models than in those required by ASHRAE 140 Standard and c) comparison to real building measured utility data. Findings and conclusions from each one of the three validation approaches are presented, as well as a discussion on model complexity vs. results accuracy based on lessons learned during the reported study.
机译:众所周知,住宅和商业建筑占美国整体能源的约40%,以及同一百分比的CO_2排放量。改造现有的旧建筑,占建筑物的99%,代表了实现挑战能源和减排目标的最佳机会。 United Technologies Research Center(UTRC)开发了一种方法和工具,为建筑改造提供了对分析和决策的计算支持。该工具基于简化的基于物理的模型,并结合了智能默认能力,自动模型校准和包装选择,以及在预测能耗和潜在节省的情况下的不确定性量化和灵敏度分析(UQ / SA)。 UQ / SA用于更好地为决策​​者提供用于分析的数据的质量,并将其引导在整个过程中,以在分析中实现所需的准确性。本文解决了简化的基于物理模型的验证。验证是使用三层方法进行的:a)针对Ashrae 140最佳案例的验证; b)使用比Ashrae 140标准和c)与真实构建测量的公用事业数据相比,使用更详细的模型获得的其他更复杂的工具获得的模式的模型比较。提出了三种验证方法中的每一个的调查结果和结论,以及基于报告研究中学到的经验教训的模型复杂性与结果精度的讨论。

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