Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter,Building Simulation |
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Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter,Building Simulation
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Individual thermal comfort models based on physiological parameters could improve the efficiency of the personal thermal comfort control system. However, the effect of thermal history has not been fully addressed in these models. In this study, climate chamber experiments were conducted in winter using 32 subjects who have different indoor and outdoor thermal histories. Two kinds of thermal conditions were investigated: the temperature dropping (24–16 °C) and severe cold (12 °C) conditions. A simplified method using historical air temperature to quantify the thermal history was proposed and used to predict thermal comfort and thermal demand from physical or physiological parameters. Results show the accuracies of individual thermal sensation prediction was low to about 30% by using the PMV index in cold environments of this study. Base on the sensitivity and reliability of physiological responses, five local skin temperatures (at hand, calf, head, arm and thigh) and the heart rate are optimal input parameters for the individual thermal comfort model. With the proposed historical air temperature as an additional input, the general accuracies using classification tree model C5.0 were increased up by 15.5% for thermal comfort prediction and up by 29.8% for thermal demand prediction. Thus, when predicting thermal demands in winter, the factor of thermal history should be considered.
中文翻译：
基于生理参数的个体热舒适模型可以提高个体热舒适控制系统的效率。然而，在这些模型中还没有完全解决热历史的影响。在这项研究中，气候室实验在冬季使用 32 名具有不同室内和室外热历史的受试者进行。研究了两种热条件：温度下降（24-16°C）和严寒（12°C）条件。提出了一种使用历史气温来量化热历史的简化方法，并用于从物理或生理参数预测热舒适度和热需求。结果表明，在本研究的寒冷环境中使用 PMV 指数，个体热感觉预测的准确率低至 30% 左右。基于生理反应的敏感性和可靠性，五个局部皮肤温度（手部、小腿、头部、手臂和大腿）和心率是个体热舒适模型的最佳输入参数。将建议的历史气温作为附加输入，使用分类树模型 C5.0 的一般准确度在热舒适度预测方面提高了 15.5%，在热需求预测方面提高了 29.8%。因此，在预测冬季热需求时，应考虑热历史因素。使用分类树模型 C5.0 进行热舒适预测的总体准确度提高了 15.5%，热需求预测的总体准确度提高了 29.8%。因此，在预测冬季热需求时，应考虑热历史因素。使用分类树模型 C5.0 进行热舒适预测的总体准确度提高了 15.5%，热需求预测的总体准确度提高了 29.8%。因此，在预测冬季热需求时，应考虑热历史因素。 |
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