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This paper investigates a method to improve buildings' thermal predictivecontrol performance via online identification and excitation (active learningprocess) that minimally disrupts normal operations. In previous studies we havedemonstrated scalable methods to acquire multi-zone thermal models of passivebuildings using a gray-box approach that leverages building topology andmeasurement data. Here we extend the method to multi-zone actively controlledbuildings and examine how to improve the thermal model estimation by using thecontroller to excite unknown portions of the building's dynamics. Comparingagainst a baseline thermostat controller, we demonstrate the utility of boththe initially acquired and improved thermal models within a Model PredictiveControl (MPC) framework, which anticipates weather uncertainty and time-varyingtemperature set-points. A simulation study demonstrates self-excitationimproves model estimation, which corresponds to improved MPC energy savings andoccupant comfort. By coupling building topology, estimation, and controlroutines into a single online framework, we have demonstrated the potential forlow-cost scalable methods to actively learn and control buildings to ensureoccupant comfort and minimize energy usage, all while using the existingbuilding's HVAC sensors and hardware.

Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings