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Use of First Law Energy Balance as a Screening Tool for Building Energy Use Data: Experiences on the Inclusion of Outside Air Enthalpy Variable

机译:使用第一定律的能量平衡作为建筑能耗数据的筛选工具:包含室外空气焓变的经验

摘要

Quality controlled energy-use data is the foundation of energy performance evaluation for a building. The ?Energy Balance Load? (EBL), a parameter derived from the first law of thermodynamics based on a whole-building energy analysis, has been theoretically proved to be an effective tool for verifying whole-building energy-use data (Shao and Claridge, 2006). Quality control methodology using EBL has been proposed and applied to more than one hundred buildings on a large university campus by Baltazar et al. (2007). They picked the outside air dry-bulb temperature (TOA) as the explanatory variable of EBL, and used a plot of EBL versus TOA, called energy balance plot, to find faulty behavior in the data by visually observing the pattern. It has been demonstrated that this methodology can detect significant data problems caused by variety of reasons such as scale factor error and mislabeled meter successfully.This paper presents a possible enhancement on the existent EBL analysis technique by using the outside air enthalpy (hOA) as the explanatory variable of EBL instead of TOA. This enthalpy based analysis accounts for the effect of latent load on EBL, and therefore, may enhance the data screening capability for buildings operated at locations with hot and humid climate. Numerical threshold of data screening proposed by Masuda et al. (2008) has been applied to this enthalpy based methodology to determine the difference in the results of data screening between enthalpy based analysis and temperature based analysis.
机译:质量受控的能源使用数据是建筑物能源绩效评估的基础。 “能量平衡负载” (EBL)是从热力学第一定律导出的一个参数,该参数基于整个建筑物的能源分析,在理论上已被证明是验证整个建筑物能源使用数据的有效工具(Shao和Claridge,2006年)。 Baltazar等人已经提出了使用EBL的质量控制方法,并将其应用于大型大学校园中的一百多栋建筑物。 (2007)。他们选择外部空气干球温度(TOA)作为EBL的解释变量,并使用EBL与TOA的关系图(称为能量平衡图)通过肉眼观察模式来发现数据中的错误行为。事实证明,该方法可以成功检测出由于各种原因而导致的重大数据问题,例如比例因子误差和仪表标记错误等。本文提出了一种通过使用外部空气焓(hOA)作为现有EBL分析技术的可能的增强方法。 EBL的解释变量,而不是TOA。这种基于焓的分析考虑了潜在负载对EBL的影响,因此可以增强在炎热潮湿的地方运行的建筑物的数据筛选功能。 Masuda等人提出的数据筛选的数值阈值。 (2008年)已被应用于这种基于焓的方法,以确定基于焓的分析与基于温度的分析之间的数据筛选结果的差异。

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