本文提出了一种数据驱动的分析方法，利用递归最小二乘（RLS）滤波器和累积和（CUSUM）检验检测异常能量数据。这种基于模型的方法将参考模型通过RLS滤波器估计的预测值与实际值进行比较，如果差值超过规定的阈值，CUSUM测试将发出警报。本文将该方法应用于基于每日室外空气温度和潜在负荷变量的整个建筑能量平衡负荷（EBL）分析。 在一年期间，15栋样本建筑的RLS滤波器的均方根误差（RMSE）与回归解的均方根误差（RMSE）之比在0.69到0.97之间。在这两个案例研究中，在第四天检测到冷冻水表的温度漂移，在问题开始出现后的第七天检测到被占用/未被占用的停用真空表。更新参考模型以考虑建筑物的动态使用和运行一直是实施现有模型的一个挑战- 基于故障检测的方法。该方法能够自动跟踪时变参数，并且在保持参考模型的预测性能方面花费较少的精力。引文:伊利诺伊州芝加哥ASHRAE交易——第121卷第1部分

This paper proposes a data-driven analysis method to detect abnormal energy data using the recursive least squares (RLS) filter and the cumulative sum (CUSUM) test. This model-based method compares the value predicted by a reference model estimated with the RLS filter and the actual value, and the CUSUM test gives an alarm if the difference exceeds the prescribed threshold. In this paper, the method is applied to the whole-building energy balance load (EBL) analysis using outdoor-air temperature and latent load variables on a daily basis. The ratios of the root-mean-square error (RMSE) of the RLS filters to the RMSE of the regression solutions for 15 sample buildings during a one-year period range from 0.69 to 0.97. In the two case studies, the temperature drift of a chilled-water meter was detected on the fourth day, and disabled occupied/unoccupiedHVACschedules were detected on the seventh day after the problems started to appear. Updating reference models to account for dynamic use and operations of buildings has been a challenge in the implementation of the existing model-based fault-detection methods. The proposed method can track time-varying parameters automatically and requires less effort to maintain the prediction performance of the reference models.