已经为暖通空调系统开发了许多故障检测和诊断系统技术，但大多数技术侧重于在部件级别检测故障，例如空气处理装置或可变风量箱。本文探讨了ASHRAE简化能量分析程序（SEAP）在整个建筑层故障检测中的应用。在所检查的程序中，SEAP的实现被“校准”为一段时间的建筑物测量加热和冷却数据，因此模拟数据与测量数据密切相关。对模拟数据进行小的调整，使调整后的模拟加热和冷却消耗总量与同期测量的加热和冷却消耗总量完全匹配。 然后，利用未来的天气数据，将校准的SEAP模拟的调整版本用于预测未来的消耗量。与未来测量数据的目视比较用于诊断与预期性能的重大偏差。该程序作为一项测试，回顾性地应用于三年的测量消耗数据。它清楚地确定了测试期间发生的三个重大操作变化。针对故障识别测试了三种不同的表示格式——月度偏差、每日百分比偏差和累积偏差图。 所有这些都有价值，最终取决于用户偏好，哪一个信息量最大。单位:SICitation:ASHRAE Transactions，第113卷，pt。2.

Numerous fault detection and diagnostic system techniques have been developed for HVAC systems, but most focus on detecting faults at the component level, for example, airhandling units or variable air volume boxes. This paper examines the use of the ASHRAE simplified energy analysis procedure (SEAP) for fault detection at the whole-building level. In the procedure examined, an implementation of the SEAP is “calibrated” to a period of measured heating and cooling data from a building so the simulated data closely follow the measured data. A small adjustment is added to the simulated data so the total adjusted simulated heating and cooling consumption values exactly match the measured heating and cooling consumption totals for the same period. The adjusted version of the calibrated SEAP simulation is then used to predict future consumption, using future weather data. Visual comparison with future measured data is used to diagnose significant deviations from expected performance. The procedure is applied retrospectively to three years of measured consumption data as a test. It clearly identifies three significant operational changes that occurred during the test period. Three different presentation formats are tested for fault identification—monthly deviations, daily percent deviations, and cumulative deviation plots. All have value, and it is ultimately a user preference as to which is the most informative.Units: SI