Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems
AbstractThis paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR) is used to obtain the input-output (I/O) curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU) rate. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers. View Full-Text
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Lin, W.-M.; Tu, C.-S.; Tsai, M.-T.; Lo, C.-C. Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems. Energies 2015, 8, 10504-10521.
Lin W-M, Tu C-S, Tsai M-T, Lo C-C. Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems. Energies. 2015; 8(9):10504-10521.Chicago/Turabian Style
Lin, Whei-Min; Tu, Chia-Sheng; Tsai, Ming-Tang; Lo, Chi-Chun. 2015. "Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems." Energies 8, no. 9: 10504-10521.