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Optimal Chiller Loading by MOEA/D for Reducing Energy Consumption

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Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

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Abstract

A modified multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to solve the optimal chiller loading (OCL) problem. In a multi-chiller system, the chillers are usually partially loaded for most of the running time. If the chillers are unreasonably managed, their consumption noticeably increases. To reduce power consumption, the partial load ratio (PLR) of each chiller must be adjusted. The system must meet the system cooling load (CL), so, it is a constrained optimization problem. This study uses a multi-objective method to solve the constrained optimization. The constraint condition is changed to a new objective, so, the problem can be solved as a multi-objective problem. Comparison with the experimental results in the literature proved the effectiveness and performance of the modified algorithm, which can be fully applied in air conditioning system operations.

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Acknowledgments

This research is partially supported by National Science Foundation of China under Grant 61773192, 61773246 and 61503170, Shandong Province Higher Educational Science and Technology Program (J17KZ005), Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2017-02), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

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Authors and Affiliations

  1. School of Computer, Liaocheng University, Liaocheng, 252059, China

    Yong Wang, Jun-qing Li, Mei-xian Song & Li Li

  2. School of Information, Shandong Normal University, Jinan, 250014, China

    Jun-qing Li & Pei-yong Duan

  3. China Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, 211189, People’s Republic of China

    Jun-qing Li

  4. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Jun-qing Li

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  1. Yong Wang
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  2. Jun-qing Li
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  3. Mei-xian Song
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  4. Li Li
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  5. Pei-yong Duan
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Corresponding authors

Correspondence to Jun-qing Li or Pei-yong Duan .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Wang, Y., Li, Jq., Song, Mx., Li, L., Duan, Py. (2018). Optimal Chiller Loading by MOEA/D for Reducing Energy Consumption. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_77

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_77

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  • Publisher Name: Springer, Cham

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