Parameter Input Estimation and Temperature Control of RC Thermal Dynamic Systems
Creators: Zamani, Vahid
Publisher: name: University of Alberta Library
Year: 2023

<p>The world&#39;s energy consumption and greenhouse gas emissions are on the rise due to increasing energy usage in buildings. To address this problem, it is important to design energy-efficient buildings with advanced control systems. One key aspect of these systems is having reliable thermal dynamic models can provide necessary information for the control systems. Thermal resistor-capacitor (RC) models are one of the commonly used methods for thermal dynamic modeling in control systems. RC models represent the thermal behavior of building components using parameters related to fundamental physical principles. The R&#39;s and C&#39;s in the model are equivalent thermal resistors and capacitors that represent the effective resistance to heat flow and thermal energy storage capacity of the nodes, respectively. These model parameters are used to relate system inputs (e.g., heating and cooling supply) and temperature states. RC model parameters and inputs (e.g., actual solar heat gain) are difficult to be directly measured but can be estimated with historical data. Accordingly, this thesis aims to develop a practical and dependable method for estimating RC model parameters and input simultaneously with and without partially missing states. By estimating model parameters and creating a trustable thermal dynamic model the developed method will be used to obtain required heating, ventilation, and air conditioning (HVAC) outputs for temperature control purposes.To estimate unknown parameters and inputs, the method uses unscented Kalman filter (UKF) in combination with nonlinear least square (NLS) estimation method. To evaluate the effectiveness of this method, two case studies are conducted. The first case study involves made-up data, while the second case study uses real-world data from a single detached house. The performance of the method is assessed by comparing estimated parameter and input values to true values in the made-up case, as well as the accuracy of the updated thermal dynamic model (created based on the last estimated model parameters) in predicting temperature responses in the real-word case. Both estimation and prediction studies indicate that the developed method can accurately estimate the unknown model inputs and parameters.The method can also be used to estimate the required heating and cooling supply for controlling the temperatures of multiple zones. To evaluate the effectiveness of this approach, two case studies are conducted: one with made-up data and one with real-world data from a single detached house. The performance of the method is assessed by simulating the thermal model with applying the estimated heating and cooling supply to the model and generate the system response for the zone temperature that requires control and verify whether the controlled zone&#39;s temperature meets the expected temperature or not. The results of the two case studies indicate that the method can accurately estimate the heating and cooling supply.Overall, the primary objective of this thesis is to develop a practical and dependable method for estimating RC model parameters and inputs simultaneously, even when some states are partially missing. Furthermore, the developed method will be used to obtain the required HVAC outputs for temperature control purposes. Achieving these goals will not only advance the relevant field but also provide feasible solutions for real-world applications.</p>