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Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

机译：基于强度多目标粒子群算法的暖通空调系统优化

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摘要

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.
机译：提出了一种数据驱动的方法，用于优化办公大楼中的供暖，通风和空调（HVAC）系统。神经网络（NN）算法优于本文研究的其他五种算法，因此可用于构建预测模型。然后，使用强度多目标粒子群优化（S-MOPSO）算法优化基于NN的预测模型。讨论了能耗和热舒适度之间随温度和湿度测量的关系。通过优化模型得出的控制设置可将能耗降至最低，同时将热舒适度保持在可接受的水平。 S-MOPSO算法得出的解决方案指出了HVAC系统的大量控制替代方案，这代表了热舒适性和能耗之间的一系列权衡。

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  • 来源
    《Energy》 |2011年第10期|p.5935-5943|共9页
  • 作者

    Andrew Kusiak; Guanglin Xu; Fan Tang;

  • 作者单位

    Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA;

    Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA;

    Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    HVAC; optimization; neutral network; evolutionary computation; strength multi-objective particle-swarm; algorithm;

    机译：暖通空调;优化;中立网络;进化计算强度多目标粒子群算法;

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