Abstract
An iterative learning (IL) approach to disturbance prediction for economic model predictive control (EMPC) is proposed and applied to an integrated solar thermal system (ISTS). The disturbance in the system, which is the user hot water demand, is predicted iteratively by taking advantage of the repetitive nature of hot water consumption and utilized by EMPC for improved ISTS control performance. Various user load scenarios are developed for simulations based on historical data, and the performance of the proposed control method is compared against an idealistic EMPC scheme with perfect load information along with existing EMPC methods and a baseline proportional-integral controller. It is demonstrated that the proposed IL approach to EMPC achieves electrical costs within 0.5% of the idealistic case while outperforming all other methods in both energy savings and output temperature management.
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