An iterative learning approach to economic model predictive control for an integrated solar thermal system
Year: 2020
Type: article
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.
Source: IFAC-PapersOnLine
Authors: Jacob Morrison, Ryozo Nagamune, Vladimir Grebenyuk
Institution: University of British Columbia
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3154873886