Modeling and control of hydronic building HVAC systems

Energy requirements for heating and cooling of residential, commercial and industrial spaces constitute a major fraction of end use energy consumed. Centralized systems such as hydronic networks are becoming increasingly popular to meet those requirements. Energy efficient operation of such systems requires intelligent energy management strategies, which necessitates an understanding of the complex dynamical interactions among its components from a mathematical and physical perspective. In this work, concepts from linear graph theory are applied to model complex hydronic networks. Further, time-scale decomposition techniques have been employed to obtain a more succinct representation of the overall system dynamics. The proposed model is then used to design predictive control strategies which are compared with traditional feedback control schemes using a simulated chilled water system as a case study. The advantages and limitations associated with these methodologies has been demonstrated. The cornerstone of this work is the development of a novel, distributed predictive scheme which provides the best compromise in the multidimensional evaluation framework of 'regulation', `optimality', `reliability' and `computational complexity'