Semi-automated modular modeling of buildings for model predictive control

Abstract

A promising alternative to standard control strategies for heating, ventilation, air conditioning and blinds positioning of buildings is Model Predictive Control (MPC). Key to MPC is having a sufficiently simple (preferably linear) model of the building's thermal dynamics. In this paper we propose and test a general approach to derive MPC compatible models consisting of the following steps: First, we use standard geometry and construction data to derive in an automated way a physical first-principles based linear model of the building's thermal dynamics. This describes the evolution of room, wall, floor and ceiling temperatures on a per zone level as a function of external heat fluxes (e.g., solar gains, heating/cooling system heat fluxes etc.). Second, we model the external heat fluxes as linear functions of control inputs and predictable disturbances. Third, we tune a limited number of physically meaningful parameters. Finally, we use model reduction to derive a low-order model that is suitable for MPC. The full-scale and low-order models were tuned with and compared to a validated EnergyPlus building simulation software model. The approach was successfully applied to the modeling of a representative Swiss office building. The proposed modular approach flexibly supports stepwise model refinements and integration of models for the building's technical subsystems.