A fast-POD model for simulation and control of indoor thermal environment of buildings

Precise and efficient control strategies of heating, ventilation, and air conditioning (HVAC) systems need detailed and dynamic indoor environment information, which is hardly acquired satisfying realtime and precision requirements simultaneously. In this study, a fast simulation method based on existing proper orthogonal decomposition (POD) is proposed for dynamic modelling and control of indoor temperature distributions. To meet the realtime and precision requirements at the same time, an offline-online scheme is applied. In the offline stage, the finite volume method (FVM) is used for spatial and temporal discretizations of the indoor temperature distributions. The obtained ordinary differential equations (ODEs) are further order-reduced by POD (Karhunen-Loève)/Galerkin techniques. Snapshot method is used for the reduced-order basis construction. In the online stage, the model predictive control (MPC) strategy is used for the purpose of reference trajectory tracking, within which the proposed POD model is embedded to realtime estimate spacial temperature variation. Both transient and steady performances of the reduced-order model are compared with those of CFD-based simulation. A boundary control test is finally given, which demonstrates the applicability of the technique.