Online Model Estimation for Predictive Thermal Control of Buildings
This addresses the challenge of scalable, cost-effective model estimation for building HVAC systems, which has hindered widespread deployment of predictive controls, though it appears incremental as an extension of prior work.
The study tackles the problem of acquiring accurate thermal models for buildings to enable predictive control, proposing a gray-box method using an Unscented Kalman Filter that learns parameters and predicts energy usage over 24+ hours, validated with simulation data.
This study 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 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 (HVAC) systems have shown significant energy savings with advanced model predictive control (MPC). 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 deployment of on-line data-driven system identification and parameter estimation routines. We propose a novel gray-box approach using an Unscented Kalman Filter based on a multi-zone thermal network and validate it with EnergyPlus simulation data. The filter quickly learns 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 study extends our initial investigation by formalizing parameter and disturbance estimation routines and demonstrating results across a year-long study.