Energy submetering at the equipment level provides a tool to identify energy use anomalies and detect operational inefficiencies. While physical meters can be costly and difficult to install, virtual meters (VMs) overcome practical issues associated with physical meters and provide insights into critical unmeasured quantities. This article introduces an inverse greybox model-based virtual metering method to estimate the energy in an air handling unit (AHU). Models that represent the components typically found in AHUs are formulated using a data set from a highly instrumented AHU and combined into an integrated greybox model. The use of the integrated model to create VMs is demonstrated by using a data set from an independent AHU located in a large office building in Ottawa, ON, Canada. Model parameters are estimated by using the genetic algorithm and used in creating VMs that can estimate the heat supplied/extracted at the AHU level. In addition, the model is used to estimate a monthly average outdoor air fraction used by the AHU. The potential of the component models and VMs to detect operational inefficiencies and support operational decisions is demonstrated through illustrative examples. Note to Practitioners-This article presents a novel virtual metering algorithm to estimate the heating and cooling energy at the air handling unit (AHU) level. This virtual metering algorithm fills a gap in the literature and provides a tool that will help detect and interpret energy use anomalies, identify operational inefficiencies, and guide on-going commissioning of building energy systems. Facility managers, operators, and other stakeholders can use the insights gained from virtual metering to improve building operational performance. Future planned research includes developing virtual meters to characterize energy flows at the zone level and visualization methods to make inverse modeling results more accessible to different building stakeholders.

中文翻译：

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空气处理机组基于逆灰盒模型的虚拟仪表的开发


设备层面的能源分项计量提供了一种识别能源使用异常和检测运营效率低下的工具。虽然物理仪表成本高昂且难以安装，但虚拟仪表 (VM) 克服了与物理仪表相关的实际问题，并提供了对关键的未测量量的洞察。本文介绍了一种基于逆灰盒模型的虚拟计量方法来估算空气处理机组 (AHU) 中的能量。代表 AHU 中常见组件的模型是使用高度仪表化的 AHU 中的数据集制定的，并组合成集成的灰盒模型。通过使用位于加拿大安大略省渥太华市一座大型办公楼内的独立 AHU 的数据集，演示了如何使用集成模型创建虚拟机。模型参数通过使用遗传算法进行估计，并用于创建可以估计 AHU 级别的供热/提取热量的 VM。此外，该模型还用于估计 AHU 使用的每月平均室外空气比例。通过说明性示例演示了组件模型和虚拟机检测运营效率低下并支持运营决策的潜力。从业者注意事项 - 本文提出了一种新颖的虚拟计量算法，用于估计空气处理机组 (AHU) 级别的加热和冷却能量。这种虚拟计量算法填补了文献中的空白，并提供了一种工具，可以帮助检测和解释能源使用异常、识别运行效率低下并指导建筑能源系统的持续调试。设施经理、运营商和其他利益相关者可以利用从虚拟计量中获得的见解来提高建筑运营绩效。 未来计划的研究包括开发虚拟仪表来表征区域级别的能量流，以及开发可视化方法以使不同建筑利益相关者更容易获得逆向建模结果。