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Optimal Chiller Loading for Energy Conservation Using an Improved Fruit Fly Optimization Algorithm

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  • Min-Yong Qi

    (College of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Jun-Qing Li

    (College of Computer Science, Liaocheng University, Liaocheng 252059, China
    School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China)

  • Yu-Yan Han

    (College of Computer Science, Liaocheng University, Liaocheng 252059, China)

  • Jin-Xin Dong

    (College of Computer Science, Liaocheng University, Liaocheng 252059, China)

Abstract

In the multi-chiller of the air conditioning system, the optimal chiller loading (OCL) is an important research topic. This research is to find the appropriate partial load ratio ( PLR ) for each chiller in order to minimize the total energy consumption of the multi-chiller under the system cooling load ( CL ) requirements. However, this optimization problem has not been well studied. In this paper, in order to solve the OCL problem, we propose an improved fruit fly optimization algorithm (IFOA). A linear generation mechanism is developed to uniformly generate candidate solutions, and a new dynamic search radius method is employed to balance the local and global search ability of IFOA. To empirically evaluate the performance of the proposed IFOA, a number of comparative experiments are conducted on three well-known cases. The experimental results show that IFOA found 14 optimal values (the optimal values among all algorithms) under a total of 17 CL s in three cases, and the ratio of the optimal values found was 82.4%, which was the highest among all algorithms. In addition, the mean value of all objective functions of IFOA is smaller and the standard deviation is equal to or close to 0, which proves that the algorithm has high stability. It can be concluded that IFOA is an ideal method to solve the OCL problem.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3760-:d:387824
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    References listed on IDEAS

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    1. 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.
    2. Chang, Yung-Chung & Chan, Tien-Shun & Lee, Wen-Shing, 2010. "Economic dispatch of chiller plant by gradient method for saving energy," Applied Energy, Elsevier, vol. 87(4), pages 1096-1101, April.
    3. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    4. 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.
    5. 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.
    6. 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.

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