We introduce the sensor-utility-network (SUN) method for occupancy estimation in buildings. Based on inputs from a variety of sensor measurements, along with historical data regarding building utilization, the SUN estimator produces occupancy estimates through the solution of a receding-horizon convex optimization problem. State-of-the-art on-line occupancy algorithms rely on indirect measurements, such as CO 2  levels, or people counting sensors which are subject to significant errors and cost. The newly proposed method was evaluated via experiments in an office building environment. Estimation accuracy is shown to improve significantly when all available data is incorporated in the estimator. In particular, it is found that the average estimation error at the building level is reduced from 70% to 11% using the SUN estimator, when compared to the naive approach that relies solely on flow measurements.
['Sean P. Meyn', 'Amit Surana', 'Yiqing Lin', 'Stella Maris Oggianu', 'Satish Narayanan', 'Thomas A. Frewen']
A sensor-utility-network method for estimation of occupancy in buildings