Fault-impact analysis (FIA) in heating, ventilation, and air conditioning (HVAC) systems involves forecasting system loads in the absence of equipment malfunction and inappropriate sequences of operations with the intention of setting a target for optimal operating energy use and encouraging and augmenting fault correction. Fault correction is an ongoing and resource-intensive endeavor for operations personnel, often motivated by occupant complaints rather than to mitigate excessive operating energy use. Thus, projecting the energy-use impact of faults is imperative to improving building energy efficiency as it leverages the potential to reduce energy use for real-time operational decision-making. Thermal energy meters (i.e., physical meters) can provide post-correction validation by quantifying the energy-use impact of faults, though are incapable of projecting this information before faults are corrected and providing motivation. Additionally, their installation and maintenance costs in existing buildings are often prohibitive. Virtual meters (VMs) which leverage HVAC controls data offer a cost-effective alternative to physical meters. Furthermore, inverse-model (IM)-based VMs enable scalable FIA by employing derived IMs at the system and zone level to emulate alternative control scenarios. This paper presents the first ever field implementation of FIA-capable VM algorithms. An automated and BAS-integrated VM algorithm was deployed in a living-lab facility in Ottawa, Canada, and the VM-estimated energy-use impact of correcting common soft faults is presented and compared with savings reported by thermal meters and savings projected by the FIA. For combined system- and zone-level heating, VMs estimated 85% of the measured energy-use savings, and a 65% reduction in energy use was projected prior to correcting faults where a 62% reduction was realized after faults were corrected. VMs can appropriately assess and project energy savings for fault correction so long as the method to baseline pre-correction energy use persists after correction.

中文翻译：

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预测和估算通过纠正控制故障节省的 HVAC 能源：物理计量和虚拟计量方法之间的比较


供暖、通风和空调 （HVAC） 系统中的故障影响分析 （FIA） 涉及在没有设备故障和不适当的操作顺序的情况下预测系统负载，目的是为最佳运行能源使用设定目标，并鼓励和增强故障纠正。故障纠正是运营人员的一项持续的、耗费资源的工作，其动机通常是居住者的投诉，而不是为了减少过度的运营能源使用。因此，预测故障对能源使用的影响对于提高建筑能源效率至关重要，因为它利用了减少能源使用的潜力来做出实时运营决策。热能表（即物理仪表）可以通过量化故障的能源使用影响来提供校正后验证，但无法在故障被纠正之前投射这些信息并提供动力。此外，它们在现有建筑物中的安装和维护成本通常高得令人望而却步。利用 HVAC 控制数据的虚拟电表 （VM） 为物理电表提供了一种经济高效的替代方案。此外，基于逆模型 （IM） 的 VM 通过在系统和区域级别使用派生的 IM 来模拟替代控制场景，从而实现可扩展的 FIA。本文介绍了支持 FIA 的 VM 算法的首次现场实现。在加拿大渥太华的一个生活实验室设施中部署了自动化和 BAS 集成的 VM 算法，并提出了纠正常见软故障的 VM 估计的能源使用影响，并与热表报告的节省和 FIA 预测的节省进行了比较。 对于系统级和区域级联合供暖，VM 估计测量的能源使用节省量为 85%，在纠正故障之前预计能源使用量减少 65%，而在纠正故障后减少了 62%。只要在纠正后仍坚持使用基线修正前能源使用的方法，VM 就可以适当地评估和预测故障纠正的能源节省。