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HVAC system modeling and optimization: A data-mining approach.

机译:暖通空调系统建模和优化:一种数据挖掘方法。

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摘要

Heating, ventilating and air-conditioning (HVAC) system is a complex non-linear system with multi-variables simultaneously contributing to the system process. It poses challenges for both system modeling and performance optimization. Traditional modeling methods based on statistical or mathematical functions limit the characteristics of system operation and management.;Data-driven models have shown powerful strength in non-linear system modeling and complex pattern recognition. Sufficient successful applications of data mining have proved its capability in extracting models that accurately describe the relation of inner system. The heuristic techniques such as neural networks, support vector machine, and boosting tree have largely expanded to the modeling process of HVAC system.;Evolutionary computation has rapidly merged to the center stage of solving the multi-objective optimization problem. Inspired from the biology behavior, it has shown the tremendous power in finding the optimal solution of complex problem. Different applications of evolutionary computation can be found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply the evolutionary computation approach in optimizing the performance of HVAC system. Energy saving can be achieved by implementing the optimal control setpoints with IAQ maintained at an acceptable level. A trade-off between energy saving and indoor air quality maintenance is also investigated by assigning different weights to the corresponding objective function. The major contribution of this research is to provide the optimal settings for the existing system to improve its efficiency and different preference-based operation methods to optimally utilize the resources.
机译:加热,通风和空调(HVAC)系统是一个复杂的非线性系统,其中多变量同时对系统过程有所贡献。这对系统建模和性能优化都提出了挑战。传统的基于统计或数学函数的建模方法限制了系统运行和管理的特征。数据驱动模型在非线性系统建模和复杂模式识别中显示出强大的实力。数据挖掘的成功成功应用证明了其提取模型的能力,这些模型可以准确地描述内部系统的关系。神经网络,支持向量机,Boosting树等启发式技术已在很大程度上扩展到了HVAC系统的建模过程中。进化计算已迅速融合到解决多目标优化问题的中心阶段。受到生物学行为的启发,它在发现复杂问题的最佳解决方案方面显示出了巨大的力量。演化计算的不同应用可以在商业,市场,医疗和制造领域找到。本文的重点是将进化计算方法应用于优化HVAC系统的性能。通过将IAQ维持在可接受的水平上来实现最佳控制设定点,可以实现节能。通过为相应的目标函数分配不同的权重,还研究了节能与室内空气质量维护之间的权衡。这项研究的主要贡献是为现有系统提供最佳设置以提高其效率,并提供不同的基于偏好的操作方法以最佳地利用资源。

著录项

  • 作者

    Tang, Fan.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Engineering Industrial.
  • 学位 M.S.
  • 年度 2010
  • 页码 105 p.
  • 总页数 105
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:54

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