Buildings are one of the largest energy consumers around the world. Towards reducing the energy wastage in buildings, occupancy based control systems are becoming more wide-spread in commercial office buildings. Accurate identification of occupancy is crucial for such automated energy monitoring systems and for other potential applications such as personal comfort, air quality, and energy auditing. However, efficient and accurate ways to identifying occupancy in large scale buildings is a challenging task. Several approaches have been studied in literature ranging from direct to indirect approaches. In this paper, we present a nonintrusive and indirect occupancy detection approach using environmental sensor data as proxy. Specially, we used temperature and Carbon dioxide sensor dataset available in public domain. We employed the wide used k-means clustered algorithm for identifying occupied and unoccupied state (binary occupancy) of an office room. The proposed approach is validated on a public dataset with one week of environmental sensor data and results are analyzed using confusion matrix. Our experimental results show that the accuracy of detecting binary occupancy is 87.57%. We planned to extend our approach using other environmental sensor data.

Smart buildings, energy management, occupancy detection, data mining, and clustering

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