Adaptive model predictive control of building hvac using moving horizon estimation

Abstract

A differentiable physics model of a building is used that defines thermodynamic relationships between zones of the building and a heating, ventilation, and air-conditioning (HVAC) system. A physics-constrained, data driven model learns behaviors of controlled components of the HVAC system. For each of a series of times during online operation of the HVAC system, past state values are recorded representing a performance of the HVAC system in the building and past inputs to the HVAC system to maintain the states. The past state values and the past inputs are input into the differentiable physics model and the data driven model to: jointly update first parameters of the differentiable physics model and second parameters of the data driven model, e.g., using moving horizon estimation; and determine a current input to the controlled components, e.g., using model predictive control.