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Thermal comfort of naturally ventilated houses in countryside of subtropical region

机译:亚热带地区农村自然通风房屋的热舒适性

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Based on field study and Site investigations, this paper obtains the relationship between thermal comfort and air temperature, air velocity, relative humidity and means radiant temperature of naturally ventilated houses in countryside of subtropical region. According to Fanger[1], one will feel good when the thermal comfort vote is in the interval [−1, 1]. Thus, this thesis gains the reasonable interval of temperature: [24.7°C, 31.2°C], air velocity: [0.5m/s, 1.1m/s], relative humidity: [65%, 85%] and mean radiant temperature: [17.2°C, 32.8°C]. Using the seven scale comfort index[2], the artificial neural network is designed to predict the thermal comfort. We gain six indexes in the field research and they are temperature, air velocity, relative humidity, mean radiant temperature, metabolic rate and clothing thermal resistance. These six indexes can be used as the inputs of the network, meanwhile, the output of the networks is the thermal sensation vote. In addition, forty groups of data can be used to train the network and the other seventeen groups are used to predict. Residuals are small and it proves that the effectiveness of the network is excellent.
机译:在实地研究和现场调查的基础上,获得了亚热带地区农村自然通风房屋的热舒适度与气温,风速,相对湿度和平均辐射温度之间的关系。根据Fanger [1],当热舒适度在[-1,1]区间内时,人们会感觉良好。因此,本论文获得了合理的温度区间:[24.7°C,31.2°C],风速:[0.5m / s,1.1m / s],相对湿度:[65%,85%]和平均辐射温度:[17.2℃,32.8℃]。利用七等级舒适指数[2],设计了人工神经网络来预测热舒适度。我们在现场研究中获得了六个指标,分别是温度,空气速度,相对湿度,平均辐射温度,代谢率和衣物耐热性。这六个指标可以用作网络的输入,同时网络的输出是热感投票。此外,可以使用40组数据来训练网络,而使用其他17组数据来进行预测。残差很小,证明了该网络的有效性非常好。

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