Occupancy-centric predictive control of building systems

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It is estimated that 15 to 30% of the energy used in commercial buildings is wasted due to inefficiencies in the**operation of indoor climate control systems. Given that indoor climate control in commercial buildings in**Canada accounts for 13% of the secondary energy use, 11% of the CO2 emissions, and is a major driver for**new energy infrastructure, efficient operation of buildings represents great potential to reduce our**environmental and economic impact.**The most basic requirement for energy efficient building operation is to provide building services only when**and where they are needed, in the amount that they are needed. This requirement is inherently linked to**acquiring various forms of occupancy information. For example, the information regarding the arrival and**departure times is needed to determine the operating hours for heating and cooling equipment. The information**about the number of occupants in a building is needed to determine the ventilation rates for indoor air quality.**Despite the need for accurate occupancy information in building energy management, existing occupancy**sensing technologies are either too inaccurate (e.g., motion detector-based occupancy sensors) or too invasive**causing privacy concerns (e.g., camera-based people counting sensors). To this end, this project will develop**sensor fusion-based algorithms that can leverage low-cost data streams that are commonly available in building**automation systems to detect and forecast floor level occupancy counts. The algorithms will be extensively**tested via simulation and field implementations.**The project will make significant contributions to Canada. New occupancy sensing methods will be created.**Adoption of these methods by the industry partner, a Canadian energy analytics company, will contribute to our**knowledge-based economy. Wider usage of the algorithms will reduce the environmental and economic impact**of commercial buildings. The HQP will conduct interdisciplinary research on building performance, building**automation systems, and data mining.