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首页> 外文期刊>Building and Environment >Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller
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Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller

机译：混合遗传算法的建筑环境最优设计，人工神经网络，多元回归分析和模糊逻辑控制器

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摘要

Computational cost poses a major obstacle to the design of indoor environments with the current optimal method and computational fluid dynamics (CFD). A novel optimization method integrating a genetic algorithm (GA), an artificial neural network (ANN), multivariate regression analysis (MRA), and a fuzzy logic controller (FLC) was proposed in this paper to optimize the indoor environment and energy consumption based on simulation results. Thermal comfort (predicted mean vote) was set as the restrictive design objective. Indoor air quality (air age) and energy consumption were set as the optimal design objectives. Air supply parameters, such as ventilation rate, inlet temperature, and angle, were used as the design variables. The GA process was used to search for the optimal solution (individual), while the ANN and CFD tool were used to obtain the values of the objectives for each individual. MRA was used to reduce the variable space, and FLC was used to control the execution routine of the CFD process to reduce the computational cost. The results indicated that the ventilation rate has a lower impact on the design result compared with the other two design variables. When the MRA and FLC were included in the design process, the variable space and computational cost were reduced by 50% and 35.7%, respectively. The design efficiency was improved while the best found solution was maintained.
机译：计算成本对室内环境设计的主要障碍具有当前的最佳方法和计算流体动力学（CFD）。本文提出了一种集成遗传算法（GA），人工神经网络（ANN），多变量回归分析（MRA）和模糊逻辑控制器（FLC）的新颖优化方法，以优化基于的室内环境和能耗仿真结果。热舒适度（预测的平均投票）被设定为限制性的设计目标。室内空气质量（空中时代）和能源消耗被设定为最佳的设计目标。空气供应参数，如通风率，入口温度和角度，用作设计变量。 GA过程用于搜索最佳解决方案（个人），而ANN和CFD工具用于获得每个人的目标的值。 MRA用于减少可变空间，并且FLC用于控制CFD过程的执行程序以降低计算成本。结果表明，与其他两个设计变量相比，通风率对设计结果具有较低的影响。当MRA和FLC包括在设计过程中时，可变空间和计算成本分别降低了50％和35.7％。在维持最佳发现的溶液时，设计效率得到改善。

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  • 来源
    《Building and Environment》 |2020年第5期|106810.1-106810.10|共10页
  • 作者

    Zhang Tianhu; Liu Yuanjun; Rao Yandi; Li Xiaopeng; Zhao Qingxin;

  • 作者单位

    Yanshan Univ State Key Lab Metastable Mat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China|Yanshan Univ Heibei Prov Low Carbon & Clean Bldg Heating Techn Qinhuangdao 066006 Hebei Peoples R China|Yanshan Univ Key Lab Green Construct & Intelligent Maintenance Qinhuangdao 066004 Hebei Peoples R China;

    Yanshan Univ Key Lab Green Construct & Intelligent Maintenance Qinhuangdao 066004 Hebei Peoples R China;

    Yanshan Univ Key Lab Appl Chem Hebei Prov Qinhuangdao 066004 Hebei Peoples R China;

    Yanshan Univ Key Lab Green Construct & Intelligent Maintenance Qinhuangdao 066004 Hebei Peoples R China;

    Yanshan Univ State Key Lab Metastable Mat Sci & Technol Qinhuangdao 066004 Hebei Peoples R China|Yanshan Univ Key Lab Green Construct & Intelligent Maintenance Qinhuangdao 066004 Hebei Peoples R China;

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

    Artificial neural network; Genetic algorithm; Multivariate regression analysis; Fuzzy logic controller; Thermal comfort; Building energy consumption;

    机译：人工神经网络;遗传算法;多元回归分析;模糊逻辑控制器;热舒适;建筑能耗;

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