Piecewise Linear Thermal Model and Recursive Parameter Estimation of a Residential Heating System
Model predictive control (MPC) strategies show great potential for improving the performance and energy efficiency of building heating, ventilation, and air-conditioning (HVAC) systems. A challenge in the deployment of such predictive thermostatic control systems is the need to learn accurate models for the thermal characteristics of individual buildings. This necessitates the development of online and data-driven methods for system identification. In this paper, we propose a piecewise linear thermal model of a building. To learn the model, we present a Kalman filter based approach for estimating the parameters. Finally, we fit the piecewise linear model to data collected from a residential building with a forced-air heating and ventilation system and validate the accuracy of the trained model.