Abstract
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.
Highlights
The air-conditioning systems of large-scale commercial buildings in Taiwan account for about32%–54% of Taiwan’s electrical energy consumption, and chiller plants consume more than 70% of the overall energy consumed by air-conditioning systems [1]
In systems where multiple chillers are operated in parallel, each chiller can operate independently; adjusting the chiller operation schedule to provide the venue with a stable refrigeration ton (RT) load and a flexible maintenance schedule [1] is a common practice in large commercial buildings
This study proposes a two-stage differential evolution (DE) algorithm to solve optimal chiller loading (OCL) problems
Summary
The air-conditioning systems of large-scale commercial buildings in Taiwan account for about. Chang et al [5] applied an evolutionary strategy (ES) to OCL problems and found a lower power consumption with higher precision for chillers than found previously. PSO was more efficient than binary GA and real-valued GA in solving OCL problems. DE algorithms, first using exploration and a high-convergence mechanism to search for optimal solutions. This study proposes a two-stage DE algorithm to solve OCL problems. A binary encoding method enabled greater exploration than a real-valued encoding algorithm [17] would have allowed; in the process of searching for the optimal solution, the proposed method was able to quickly find the optimal solution. Through the integration of the aforementioned two stages, the proposed two-stage algorithm, which integrates the advantages of binary MBDE and real-valued DE, has excellent exploration and exploitation capabilities.
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