Abstract

We analyze high-resolution temperature measurements from commercial buildings using wireless sensors to assess the performance and health of the building’s heating, ventilation, and air conditioning (HVAC) zoning and controls system. Then we conducted two cluster analyses to evaluate the efficiency of the existing zoning structure and to find the optimal number of clusters. K-means and time series clustering were used to identify the temperature clusters per building floor. Based on statistical assessments, we observed that time series clustering showed better results than k-means clustering. We then developed a Dynamic Time Warping (DTW) based anomaly detection method to identify anomalies and introduced a scoring method to identify abnormal sensors.