Advanced Occupancy Sensing for Energy Efficiency in Office Buildings

Abstract

Control systems for Heating, Ventilation and Air-conditioning (HVAC) in non-domestic buildings often operate to fixed schedules, assuming maximum occupancy during business hours. Since lower occupancies usually mean less demand for HVAC, energy savings could be made. Air quality sensing, often combined with temperature sensing, has performed sufficiently in the past for this if maintained properly, although sensor and control failures may increase energy use by as much as 50%. As energy costs increase, building controls must meet increasingly stringent environmental requirements, increases in building services complexity, and reduced commissioning time, all placing ever higher demands on sensing, with a standing requirement to improve reliability. Sensor fusion offers performance and resilience to meet these demands, while cost and privacy are key factors which are also met. This paper describes a neural network approach to sensor fusion for occupancy estimation. Feature selection was carried out using symmetrical uncertainty analysis, while fusion of sensor features used a back-propagation neural network, with occupant count accuracy exceeding 74%