(Georgia Institute of Technology, 2020-12-08)
Lu, Di
The HVAC system consumes 30-50% of the energy delivered to a building, providing heating and cooling to maintain suitable thermal conditions for occupants. In recent years, advanced control methods, such as model predictive control (MPC), are being studied to lower building energy cost (e.g., by deferring consumption to low rate hours of the day) while still satisfying comfort requirements to an acceptable degree. Two main research gaps are identified from the literature on MPC and human thermal comfort. First, zonal control flexibility employed by MPC in terms of thermal requirements is not well defined. Second, confusion persists about the contribution of MPC vis a vis other energy conservation methods. These two research gaps weaken the acceptance of existing models and thereby frustrate the real-life application of MPC.
The objective of the undertaken research is to analyze the potential, strategy, and implementation of thermal load control with the aim to quantify its ability to minimize the operation cost of HVAC systems. This is achieved in five consecutive steps, 1) understanding zonal control flexibility, 2) evaluating the potential of building thermal load control with zonal control flexibility, 3) analyzing the potential for varying climate zones and construction types, 4) investigating the performance of MPC under scenario uncertainties, and 5) developing a thermal load control strategy that is ready for implementation. In each step, a mathematical formulation of the optimal control problem is formulated and consequently solved by appropriate algorithms. A novel comfort tolerance model for occupant cohorts is developed and implemented as constraints on the control envelope. The research outcomes expand the understanding of the multiple aspects of building thermal load control.