利用智能技术评估预测平均投票的模型开发与分析

L. Sansyzbay, B. Orazbayev, W. Wójcik
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引用次数: 0

摘要

确定小气候舒适程度的方法之一是测量其各个组成部分:温度、空气速度、相对湿度和空气质量。这种方法的一个显著缺点是忽略了小气候参数之间的相互影响。为了提高小气候舒适度的准确性,有必要采用复杂的预测平均投票(PMV)指标。PMV方程复杂,计算量大;简化的解决方案可以使用方杰的图表,Excel计算程序和专门的计算机应用程序。随着科技的发展,智能小气候系统越来越受欢迎。在本文中,为了选择最有效的智能技术之一,使用模糊逻辑和神经网络框架开发了评估PMV指标的模型。使用俄罗斯联邦国家单一企业研究所研究人员的计算程序获得的数据作为模型开发的输入参数。根据ISO 7730:2005标准中的PMV参数值验证了该程序的性能,并发现了良好的一致性。将所考虑的模型产生的PMV指数值与使用程序计算的数值进行比较,以确定所开发模型的可操作性和效率。我们的分析表明,与模糊系统相比,神经网络在热舒适评估方面表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Analysis of Models for Assessing Predicted Mean Vote Using Intelligent Technologies
One of the approaches toward determining the degree of microclimate comfort is measuring its individual components: temperature, air velocity, relative humidity, and air quality. A significant disadvantage of this approach is the neglect of the mutual influence of microclimate parameters on each other. To improve the accuracy of determining microclimate comfort, it is necessary to use a complex predicted mean vote (PMV) indicator. The PMV equation is complex and computationally consuming; simplified solutions can be obtained using Fanger’s diagrams, Excel calculation programs, and specialized computer applications. With the development of technology, intelligent microclimate systems are gaining popularity. In this article, for selecting one of the most effective intelligent technologies, models have been developed for assessing the PMV indicator using the frameworks of fuzzy logic and neural networks. The data obtained using the calculation program of the researchers of the Federal State Unitary Enterprise Research Institute (Russia) were used as input parameters for the models’ development. The program’s performance was validated against the PMV parameter values in the ISO 7730:2005 standard, and a good agreement was found. The PMV index values produced by the considered models were compared to the values calculated using the program, to determine the operability and efficiency of the developed models. Our analysis suggests that neural networks perform better on the assessment of thermal comfort, compared with fuzzy systems.
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