Identification and energy efficient control for a building: Getting inspired by MPC

This paper deals with identification of a building model based on real-life data and subsequent temperature controller design. For the identification, advanced identification technique - namely MPC Relevant Identification method - is used. This approach has the capability of providing models with better prediction performance compared to the commonly used methods. Regarding the controller part, several alternatives are proposed. First, both linear and nonlinear MPC controlling the zone temperature are designed. Although highly attractive due to promising energetic savings and thermal comfort satisfaction, MPCs demand high computational power. To overcome this issue and preserve the attractive properties of the MPC, two MPC-learned feedback controllers are proposed, one learned from LMPC and the other learned from NMPC. While remaining computationally low-cost, they improve the performance of the classical controllers towards the high-performance MPC standards. The results exploiting data from real operation of an office equipped with air handling unit situated in Lakeshore building, Michigan Tech, are presented and discussed.