Application of an Optimisation Approach for the Calibration

Heating, ventilation and air-conditioning (HVAC) accounts for up to 50% of building energy consumption, and studies have shown significant potential for savings through the utilisation of fault detection and smart predictive control in place of traditional reactive based control systems. This paper proposes a strategy for implementing intelligent model-predictive control (MPC) of HVAC systems based on calibrated high-fidelity models and real-time performance data. A genetic optimisation algorithm is proposed to improve the initial calibration of the highfidelity building energy models (BEM), and to generate, on a semi-automatic basis, the reduced-order models (ROM) on which the control optimisation algorithms are based. We also present a case study showing the application of the genetic optimisation approach on the development and calibration of a BEM for a 2,775m2 commercial building in Helsinki, Finland.