Data-driven methods for operation and maintenance of commercial buildings

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Recent findings indicate that about 30% of the energy used in commercial buildings is wasted due to poorly maintained, degraded, and improperly controlled equipment and components. Given that indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2 emissions in Canada, optimizing their operation and maintenance represents great potential to reduce our environmental impact and to provide comfortable, healthy, and productive indoor environments.**The objective of this research project is to develop data-driven decision support algorithms that guide better indoor climate control and maintenance decisions using the data inherent in building automation and control networks. New methods to detect and isolate component level faults before they begin to affect a building's comfort and energy performance will be developed. In addition, new methods to guide optimal start / stop times for heating and cooling equipment will be examined. The viability of using low-cost data streams in occupancy detection will be explored. The relationship between occupants' thermostat use behaviour patterns and their thermal comfort preferences will be investigated. The research approach entails field-scale data collection and analyses using existing controls and automation infrastructure of three office buildings in Carleton University and field trials.**The proposed research project will make significant short-term and long-term intellectual, environmental, economic, and HQP contributions to Canada. New datasets, models, and methods will be created. Adoption of these methods by the industry partner, a Canadian building data analytics company CopperTree Analytics, will contribute to our knowledge-based economy. Wider usage of the algorithms developed in this research project will reduce the environmental and economic impact of commercial buildings. The HQP will work on data from real buildings, learn their systems and components, and their shortcomings; and conduct interdisciplinary research on building physics, indoor environmental quality, building performance simulation, and data-science.**