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Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy

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  • Chang, Yung-Chung
  • Chen, Wu-Hsing

Abstract

The values of chilled water supply temperatures in chillers indicate the load distributions as the chilled water return temperatures in all chillers are the same in a decoupled air-conditioning system. This study employs the Hopfield neural network (HNN) to determine the chilled water supply temperatures in chillers, which are used to solve the optimal chiller loading (OCL) problem. A linear input–output model is utilized as a substitute for the sigmoid function, which eliminates the shortcoming of the conventional HNN method. Notably, HNN overcomes the flaw in the Lagrangian method in that the latter cannot be utilized for solving the OCL problem as its power-consumption models include non-convex functions. The chilled water supply temperatures are used as variables to be solved for a decoupled air-conditioning system and solve the problem using the HNN method to overcome the defect in the Lagrangian method. After analysis of the case study and comparison of results using these two methods, we conclude that the HNN method solves the problem of the Lagrangian method, and produces highly accurate results. The HNN method can be applied to the operation of air-conditioning systems.

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  • Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:4:p:448-456
    DOI: 10.1016/j.energy.2008.12.010
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    1. Hu, S.-C. & Chuah, Y.K., 2003. "Power consumption of semiconductor fabs in Taiwan," Energy, Elsevier, vol. 28(8), pages 895-907.
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    Cited by:

    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Liu, Xue-feng & Liu, Jin-ping & Lu, Ji-dong & Liu, Lei & Zou, Wei, 2012. "Research on operating characteristics of direct-return chilled water system controlled by variable temperature difference," Energy, Elsevier, vol. 40(1), pages 236-249.
    3. Jiaqi Cao & Shiyu Zhou & Tao Wang & Baoqi Shan & Xueping Liu, 2023. "Research on a Variable Water Supply Temperature Strategy for a Ground-Source Heat Pump System Based on TRNSYS-GENOPT (TRNOPT) Optimization," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    4. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai & Chi-Chun Lo, 2015. "Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems," Energies, MDPI, vol. 8(9), pages 1-18, September.
    5. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    6. Yani Bao & Wai Ling Lee & Jie Jia, 2018. "Exergy Analyses and Modelling of a Novel Extra-Low Temperature Dedicated Outdoor Air System," Energies, MDPI, vol. 11(5), pages 1-25, May.
    7. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    8. Ma, Zhenjun & Wang, Shengwei, 2011. "Enhancing the performance of large primary-secondary chilled water systems by using bypass check valve," Energy, Elsevier, vol. 36(1), pages 268-276.
    9. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    10. 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.

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