Online Model Estimation for Predictive Thermal Control of Buildings

This brief proposes a general, scalable method to learn control-oriented thermal models of buildings that could enable wide-scale deployment of cost-effective predictive controls. An unscented Kalman filter (UKF) augmented for parameter and disturbance estimation is shown to accurately learn and predict a building's thermal response. Recent studies of heating, ventilating, and air-conditioning systems have shown significant energy savings with advanced model predictive control. A scalable cost-effective method to readily acquire accurate, robust models of individual buildings' unique thermal envelopes has historically been elusive and hindered the widespread deployment of prediction-based control systems. Continuous commissioning and lifetime performance of these thermal models requires the deployment of online data-driven system identification and parameter estimation routines. We propose a novel gray-box approach using an UKF based on a multizone thermal network and validate it with EnergyPlus simulation data. The filter quickly learns the parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. This brief extends our initial investigation by formalizing parameter and disturbance estimation routines and demonstrating results across a year-long study.