Intermediate complexity model for Model Predictive Control of Integrated Room Automation

Abstract

Integrated Room Automation (IRA) for office buildings deals with the automated control of blinds, electric lighting, heating, cooling, and ventilation of a room or building zone. Model Predictive Control (MPC) presents a promising method to achieve energy savings thanks to improved control. This paper presents the development and validation of an intermediate complexity, integrated building and building equipment model suitable for application within MPC. A resistance-capacitance (RC) modeling approach was chosen. The result is a 12th order multiple-input-multiple-output bilinear model for the coupled simulation of the thermal, light and air quality dynamics of a single room. It includes the subsystems mechanical and natural ventilation, radiator and floor heating, cooling ceiling and thermally activated building system. For computational efficiency and compatibility with MPC, several approximations and a reduction of complexity were necessary. The resulting behaviors were validated against detailed building and system simulations. The model was found to deliver accurate and reliable results. It can be flexibly configured to represent typical building types and variants of technical systems. Furthermore, it is sufficiently efficient to support large-scale sensitivity studies. Finally, the model is ready for adaptation and integration into commercial algorithms for the control of real buildings.