Search

• A decentralized control method for multi-split air conditioning system is proposed. • Up to 8.6% energy savings are achieved with the proposed control strategy. • The EOTF-based PI controller has better robustness than model predictive controller. Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller. [ABSTRACT FROM AUTHOR]

In Taiwan, over 45% of the energy in common buildings is used for the air-conditioning system. In particular, the chiller plant consumes about 70% of the energy in air-conditioning system. The electric energy consumption of air-condition system in a clean room of semiconductor factory is about 5–10 times of that in a common building. Consequently, the optimal chiller loading in energy saving of building is a vital issue. This paper develops a new algorithm to solve optimal chiller loading (OCL) problems. The proposed two-stage differential evolution algorithm integrated the advantages of exploration (global search) in the modified binary differential evolution (MBDE) algorithm and exploitation (local search) in the real-valued differential evolution (DE) algorithm for finding the optimal solution of OCL problems. In order to show the performance of the proposed algorithm, comparison with other optimization methods has been done and analyzed. The result shows that the proposed algorithm can obtain similar or better solution in comparison to previous studies. It is a promising approach for the OCL problem. [ABSTRACT FROM AUTHOR]