Online Model Estimation for Predictive Thermal Control of Buildings

TL;DR

This paper introduces a scalable online method using an Unscented Kalman Filter to accurately learn and predict building thermal responses, enabling cost-effective predictive control for energy savings.

Contribution

It presents a novel gray-box modeling approach with an Unscented Kalman Filter for real-time thermal model estimation of buildings, facilitating widespread deployment of predictive HVAC control.

Findings

Accurately predicts thermal response over 24+ hours

Learns building-specific parameters during load periods

Demonstrates robustness over a year-long simulation

Abstract

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.