Optimization of the design and operation of sustainable and resilient buildings of 21st century

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The long-term objective of the applicant's highly original and innovative research program is the optimization of design and operation of sustainable and resilient buildings of 21st century. Resilience is the process of adapting well in the face of adversity, trauma, tragedy, threats etc. and recovering quickly from such situations. Short-term objective 1 focuses on the development of a method for Automated Detection and Diagnosis of Simultaneous Multiple Faults (ADDSMF) of heating, ventilation and air conditioning (HVAC) systems. In the applicant's vision, Building Automation Systems (BAS) installed in commercial and institutional buildings should mimic the human nervous system, with some enhanced features such as data validation, knowledge extraction, and fault detection and diagnosis (FDD). The development of such a method is based on quality of measurements from BAS, and efficient adaptive mathematical models that learn continuously from new measurements to predict the performance benchmarks, detect simultaneous faults in HVAC systems, identify and self-calibrate the faulty sensors. The research program focuses on the application of Machine Learning (ML) techniques, a subfield of artificial intelligence (AI) domain, for the development of such adaptive models. Two different classes of models are proposed: Black-box models and Gray-box models. A prototype library of ADDSMSF models will be developed. The use of such models in commercial buildings will contribute to fast detection of faults, and reduction of operation costs and propagation of viruses in buildings. The models should be integrated in BAS produced by Canadian companies, which will become more competitive, nationally and internationally. Short-term objective 2 focuses on the development of multi-objective optimization framework of HVAC systems, with application to optimum selection of reference HVAC systems of National Energy Code of Canada for Buildings (NECB). The applicant proposes the development of a large-scale multi-objective optimization framework for the selection of configuration of HVAC systems and sizing the equipment capacity. The framework will be connected with a building energy analysis program (e.g., EnergyPlus, eQUEST). This is a problem of searching for minimum life cycle energy use, life cycle cost, life cycle exergy destruction, and life cycle environmental impact, four conflicting objective functions. Design alternatives of HVAC systems that cannot protect the occupants against anticipated outdoor threats (physical, chemical and biological) are eliminated. The proposed optimization framework will be used for the selection of innovative reference HVAC systems with low energy target value, for different building types in major cities in Canada. Outcomes of this research contribute to the update of NECB.