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The influence of relative humidity on adaptive thermal comfort

机译：相对湿度对自适应热舒适度的影响

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摘要

Buildings generate nearly 30% of global carbon emissions, primarily due to the need to heat or cool them to meet acceptable indoor temperatures. In the last 20 years, the empirically derived adaptive model of thermal comfort has emerged as a powerful alternative to fixed set-point driven design. However, current adaptive standards offer a simple linear relationship between the outdoor temperature and the indoor comfort temperature, assumed to sufficiently explain the effect of all other variables, e.g. relative humidity (RH) and air velocity. The lack of a signal for RH is particularly surprising given its well-known impact on comfort. Attempts in the literature to either explain the lack of such a signal or demonstrate its existence, remain scattered, unsubstantiated and localised. In this paper we demonstrate, for the first time, that a humidity signal exists in adaptive thermal comfort using global data to form two separate lines of evidence: a meta-analysis of summary data from 63 field studies and detailed field data from 39 naturally ventilated buildings over 8 climate types. We implicate method selection in previous work as the likely cause of failure to detect this signal, by demonstrating that our chosen method has a 56% lower error rate. We derive a new designer-friendly RH-inclusive adaptive model that significantly extends the range of acceptable indoor conditions for designing low-energy naturally-conditioned buildings all over the world. This is demonstrated through parametric simulations in 13 global locations, which reveal that the current model overestimates overheating by 30% compared to the new one. (C) 2017 Elsevier Ltd. All rights reserved.
机译：建筑物产生近30％的全球碳排放量，主要是由于需要加热或冷却建筑物以达到可接受的室内温度。在过去的20年中，根据经验得出的热舒适度自适应模型已经成为固定设定点驱动设计的有力替代方案。但是，当前的自适应标准在室外温度和室内舒适温度之间提供了简单的线性关系，假定可以充分解释所有其他变量（例如温度）的影响。相对湿度（RH）和空气速度。考虑到其对舒适性的众所周知的影响，缺少RH信号尤为令人惊讶。文献中试图解释这种信号的缺乏或证明其存在的尝试仍然是分散的，没有根据的和局部的。在本文中，我们首次证明了湿度信号存在于自适应热舒适性中，使用全局数据来形成两条单独的证据线：对63个现场研究的汇总数据进行荟萃分析，以及对39个自然通风的详细现场数据进行荟萃分析超过8种气候类型的建筑物。通过证明我们选择的方法将错误率降低了56％，我们在先前的工作中暗示了方法的选择可能是未能检测到该信号的可能原因。我们推导出了一个新的，对设计人员友好的，包含RH的自适应模型，该模型显着扩展了可接受的室内条件的范围，从而可以在全世界范围内设计低能耗的自然条件建筑。通过在全球13个地点进行的参数模拟证明了这一点，这表明与新模型相比，当前模型高估了30％的过热。 （C）2017 Elsevier Ltd.保留所有权利。

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