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The Optimization of Chiller Loading by Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms

机译：自适应神经模糊推理系统和遗传算法优化冷水机组负荷

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摘要

A central air-conditioning (AC) system includes the chiller, chiller water pump, cooling water pump, cooling tower, and chilled water secondary pumps. Among these devices, the chiller consumes most power of the central AC system. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) were utilized for optimizing the chiller loading. The ANFIS could construct a power consumption model of the chiller, reduce modeling period, and maintain the accuracy. GA could optimize the chiller loading for better energy efficiency. The simulating results indicated that ANFIS combined with GA could optimize the chiller loading. The power consumption was reduced by 6.32-18.96% when partial load ratio was located at the range of 0.6 similar to 0.95. The chiller power consumption model established by ANFIS could also increase the convergence speed. Therefore, the ANFIS with GA could optimize the chiller loading for reducing power consumption.
机译：中央空调（AC）系统包括冷却器，冷却器水泵，冷却水泵，冷却塔和冷冻水二次泵。在这些设备中，冷却器消耗中央交流系统的大部分电能。本文利用自适应神经模糊推理系统（ANFIS）和遗传算法（GA）对冷水机组负荷进行优化。 ANFIS可以构建冷水机组的功耗模型，缩短建模周期，并保持精度。遗传算法可以优化冷水机组负荷，以提高能源效率。仿真结果表明，ANFIS与遗传算法相结合可以优化冷水机组的负荷。当部分负载比位于0.6的范围内（与0.95相似）时，功耗降低了6.32-18.96％。由ANFIS建立的冷水机组能耗模型也可以提高收敛速度。因此，带有GA的ANFIS可以优化冷却器负载，以降低功耗。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第12期|306401.1-306401.10|共10页
  • 作者

    Lu Jyun-Ting; Chang Yung-Chung; Ho Cheng-Yi;

  • 作者单位

    Natl Taipei Univ Technol, Dept Energy & Refrigerating Air Conditioning Engn, Taipei 10608, Taiwan.;

    Natl Taipei Univ Technol, Dept Energy & Refrigerating Air Conditioning Engn, Taipei 10608, Taiwan.;

    Natl Taipei Univ Technol, Dept Energy & Refrigerating Air Conditioning Engn, Taipei 10608, Taiwan.;

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