Scalable Cyber-Physical-Human Systems for Intelligent and Interactive Buildings

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The building sector accounts for about 30% of Canada's total end-use energy consumption, while Canadians spend 90% of their time indoors. In this context, equipping buildings with human interactive intelligent systems, i.e., cyber-physical-human systems (CPHSs), has received attention as a cost-effective solution to improve building energy performance and indoor environmental quality (IEQ). The long-term vision of the proposed research program is to mitigate climate change and advance the quality of life through the realization of intelligent and interactive buildings. Towards this vision, my approach is to pursue seamless integration of domain knowledge and machine learning (ML) to develop effective, reliable, and scalable AI-based CPHSs for intelligent and interactive buildings. Over the next five years, the overall objective of the proposed research program is to conclusively determine and demonstrate the efficacy of my pioneering approach that integrates domain knowledge and ML in developing AI-based CPHSs for intelligent and interactive buildings. My focus will initially be on office building operations. The corresponding short-term objectives are to: (1)Develop a knowledge modelling framework that enables encoding of existing building domain knowledge into CPHSs and facilitates knowledge-informed ML; (2)Develop a causal inference framework that allows investigating new causal knowledge related to occupants' needs, behaviours, and their corresponding decision-making processes; (3)Develop and demonstrate a CPHS for intelligent and interactive buildings, equipped with (i) a scalable adaptive optimal controller and (ii) an advanced user interface with an effective feedback/incentive program, by utilizing the frameworks described in Objective 1 and 2. The proposed research will revolutionize the building automation industry so that intelligent and interactive buildings become commodities resulting in a profound impact on the energy sectors in Canada. In addition, the research will contribute to advancing the quality of life in Canada with improved IEQ. The successful completion of the objectives will be a breakthrough in CPHS research, in general, and building automation and management, in particular. The proposed research will also contribute to the interdisciplinary training of highly qualified personnel who will gain knowledge and skills in two domains: (i) building energy system and occupant and (ii) machine learning, optimal control, and human-machine interaction. The unique expertise HQP acquires will allow them to take a significant role in bridging these two domains and contribute to improving building energy performance and indoor environmental quality by applying modern AI solutions to buildings.