Anomalous occupancy sensor behavior detection in connected indoor lighting systems

We consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detection (type-2 anomalies). We consider a supervised machine learning approach to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in the time and frequency domains and employ a random forest classifier to perform 3-class classification. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k-means method and a random forest classifier with a single signal energy feature

Anomalous occupancy sensor behavior detection in connected indoor lighting systems

\u3cp\u3eWe consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detection (type-2 anomalies). We consider a supervised machine learning approach to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in the time and frequency domains and employ a random forest classifier to perform 3-class classification. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k-means method and a random forest classifier with a single signal energy feature.\u3c/p\u3