Abstract Deficient performance of Predicted Mean Vote (PMV) in estimating thermal sensation in warm environments has always been considered as one of the major drawbacks of this model. This well-known shortcoming is often attributed to PMV overlooking thermal adaptation, but simplistic treatment of thermoregulatory sweating may also partly contribute to this error. In this paper a new approach to estimating sweating heat loss in the PMV model is proposed based on a piecewise fuzzy regression model. The effect of sweating at low physical activity levels and the impact of environmental parameters on whole-body evaporative heat loss are also accounted for. The new modified PMV model is validated with previously published experimental data. The result of our analysis demonstrated that the proposed model performs better than the original PMV model. The modified PMV model predicted thermal sensations more accurately than the original version of the PMV model, even without the expectancy factor being applied. This implies that a considerable part of the PMV error is associated with the simplification of sweating calculations. Ignoring the structural drawback of the original PMV calculations can lead to over-estimation of adaptation (or expectancy) coefficients when adjusting the PMV model.

中文翻译：

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修改 PMV/PPD 模型中汗液蒸发热损失以改进温暖气候下的热舒适度预测

摘要 预测平均投票（PMV）在估计温暖环境中的热感觉方面的性能不足一直被认为是该模型的主要缺点之一。这个众所周知的缺点通常归因于 PMV 忽视了热适应，但对体温调节出汗的简单处理也可能部分导致了这种错误。在本文中，基于分段模糊回归模型，提出了一种估计 PMV 模型中出汗热损失的新方法。低体力活动水平下出汗的影响以及环境参数对全身蒸发热损失的影响也被考虑在内。新修改的 PMV 模型已通过先前发布的实验数据进行验证。我们的分析结果表明，所提出的模型比原始 PMV 模型表现更好。即使没有应用预期因子，修改后的 PMV 模型也比 PMV 模型的原始版本更准确地预测热感觉。这意味着 PMV 误差的很大一部分与出汗计算的简化有关。忽略原始 PMV 计算的结构缺陷会导致在调整 PMV 模型时高估适应（或期望）系数。