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Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading

Author

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  • Chang-Ming Lin

    (Institute of Electrical and Control Engineering, National Chiao Tung University, No. 1001 University Road, Hsinchu 30010, Taiwan)

  • Chun-Yin Wu

    (Department of Mechanical Engineering, Tatung University, 40 Zhongshan North Road, 3rd Section Taipei 104, Taiwan)

  • Ko-Ying Tseng

    (Energy and Environment Research Laboratories, Industrial Technology Research Institute, Rm.820, Bldg.51, 8F, 195, Sec.4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Chih-Chiang Ku

    (Department of Mechanical Engineering, Tatung University, 40 Zhongshan North Road, 3rd Section Taipei 104, Taiwan)

  • Sheng-Fuu Lin

    (Institute of Electrical and Control Engineering, National Chiao Tung University, No. 1001 University Road, Hsinchu 30010, Taiwan)

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.

Suggested Citation

  • Chang-Ming Lin & Chun-Yin Wu & Ko-Ying Tseng & Chih-Chiang Ku & Sheng-Fuu Lin, 2019. "Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading," Energies, MDPI, vol. 12(4), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:622-:d:206266
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    References listed on IDEAS

    as
    1. Coelho, Leandro dos Santos & Klein, Carlos Eduardo & Sabat, Samrat L. & Mariani, Viviana Cocco, 2014. "Optimal chiller loading for energy conservation using a new differential cuckoo search approach," Energy, Elsevier, vol. 75(C), pages 237-243.
    2. Tan, K.C. & Chiam, S.C. & Mamun, A.A. & Goh, C.K., 2009. "Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 197(2), pages 701-713, September.
    3. Chang, Yung-Chung, 2006. "An innovative approach for demand side management—optimal chiller loading by simulated annealing," Energy, Elsevier, vol. 31(12), pages 1883-1896.
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    Cited by:

    1. Wen-Shing Lee & Wen-Hsin Lin & Chin-Chi Cheng & Chien-Yu Lin, 2021. "Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption," Energies, MDPI, vol. 14(21), pages 1-16, October.
    2. Guoying Lin & Yuyao Yang & Feng Pan & Sijian Zhang & Fen Wang & Shuai Fan, 2019. "An Optimal Energy-Saving Strategy for Home Energy Management Systems with Bounded Customer Rationality," Future Internet, MDPI, vol. 11(4), pages 1-16, April.
    3. Min-Yong Qi & Jun-Qing Li & Yu-Yan Han & Jin-Xin Dong, 2020. "Optimal Chiller Loading for Energy Conservation Using an Improved Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(15), pages 1-18, July.

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