Assessment of the Sensor-fusion Technique for Occupancy Detection in a University Office

Abstract

Since the success of developing more energy efficient buildings has a surgical dependence on the occupants' behavior and systems usage, the necessity of accounting for their correct behavior in the simulation of the early-stage buildings' design and energy consumption evaluation, is increasing rapidly. To board this necessity, a technique called sensor-fusion is being widely implemented in the building domain, to first develop accurate descriptive models for occupancy profiles, and ultimately, to be able to predict typical profiles. In this context, an office is instrumented to monitor the indoor air quality, the power consumption, and the use of the window, and air conditioning unit. The real occupancy state was monitored manually. The data analysis allowed to highlight the most relevant parameters associated with the occupancy state, based on the Spearman's correlation coefficient. The use of histograms allowed to identify an optimal sensor combination for detecting the occupancy state of the office room. The identified optimal combination groups the CO₂, power, and window state sensors, which detected the occupancy with 91.5% of accuracy.