Model-based optimal supervisory control of chilled ceiling displacement ventilation system

Abstract

The aim of this paper is to develop an optimized online supervisory control predictive tool for the chilled ceiling displacement ventilation (CC/DV) system to minimize energy consumption while creating the best indoor air quality (IAQ) and thermal comfort. The online controller is designed to operate under an optimized control strategy with five control set points. A dynamic multi-variable objective cost function is formulated for the supervisory control of the CC/DV system performance indices and constraints, and is solved using genetic algorithm. The design of the optimized controller takes into consideration the response time of three-way valves, reheat, and supply fan to employ signaled changes in set points.The developed online controller response to load changes and its ability to change system set points to optimally meet unknown load and constrains are tested and evaluated under the simulated ‘real life’ environment for a case study. It is shown that the implemented online optimized controller is robust, and its development contributes to improved CC/DV system energy efficiency.