| On-line predictive control optimizes thermal storage application comparing to conventional control methods. Optimization control methods have been developed in the last decades. But most of them are limited by perfect load and weather prediction. On-line predictive control overcomes these disadvantages by updating weather and loading information in the control process. By increasing the frequency of prediction, on-line control presents great advantage over other methods.; In this work, the available control strategies of thermal storage are reviewed base on the previous research. Then, the EnergyPlus simulation environment is described. The simulation environment is utilized to determine actions of thermal storage on-line optimal controls. Then, selected on-line control strategies are tested under laboratory conditions using full scale HVAC set-up at the University of Colorado. The measured results were compared with simulation prediction to evaluate control effect. Finally, parameters analyses were conducted to determine the impact of various factors in the on-line predictive controls. |
