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Optimal chiller loading by improved artificial fish swarm algorithm for energy saving

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  • Zheng, Zhi-xin
  • Li, Jun-qing
  • Duan, Pei-yong

Abstract

This study presents an improved artificial fish swarm algorithm (VAFSA) to solve the optimal chiller loading (OCL) problem, using minimal power consumption of chillers and cooling towers as the objective function. In the proposed algorithm, several components are developed, such as initialization method based decimal system, food concentration function, bulletin board approach, target position search mechanism, and position move method. Then, the adjustment strategy of search range of artificial fish, which combines the global search with local search, is proposed for improving the search ability of VAFSA. To testify the performance of VAFSA, three well-known case studies are tested with the comparison with other recently reported approaches. The experimental results show that VAFSA can obtain power saving compared with other approaches, and also with the competitive convergence ability. The proposed algorithm can be used as an attractive alternative method to operate air-conditioning systems.

Suggested Citation

  • Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:227-243
    DOI: 10.1016/j.matcom.2018.04.013
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhe Tian & Chuang Ye & Jie Zhu & Jide Niu & Yakai Lu, 2023. "Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    2. Yamile Díaz Torres & Paride Gullo & Hernán Hernández Herrera & Migdalia Torres del Toro & Roy Reyes Calvo & Jorge Iván Silva Ortega & Julio Gómez Sarduy, 2023. "Energy Performance Comparison of a Chiller Plant Using the Conventional Staging and the Co-Design Approach in the Early Design Phase of Hotel Buildings," Energies, MDPI, vol. 16(9), pages 1-23, April.
    3. Li, Ze & Guo, Junfei & Gao, Xinyu & Yang, Xiaohu & He, Ya-Ling, 2023. "A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers," Applied Energy, Elsevier, vol. 349(C).
    4. Lian, Kuang-Yow & Hong, Yong-Jie & Chang, Che-Wei & Su, Yu-Wei, 2022. "A novel data-driven optimal chiller loading regulator based on backward modeling approach," Applied Energy, Elsevier, vol. 327(C).
    5. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    6. Sulaiman, Mohd Herwan & Mustaffa, Zuriani, 2024. "Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach," Energy, Elsevier, vol. 297(C).
    7. Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, March.
    8. Yang, Qiangda & Dong, Ning & Zhang, Jie, 2021. "An enhanced adaptive bat algorithm for microgrid energy scheduling," Energy, Elsevier, vol. 232(C).
    9. 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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