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Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings

机译:英国居民住宅中主卧室窗户操作的随机行为模型

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This paper presents the development of stochastic models of occupants' main bedroom window operation based on measurements collected in ten UK dwellings over a period of a year. The study uses multivariate logistic regression to understand the probability of opening and closing windows based on indoor and outdoor environment factors (physical environmental drivers) and according to the time of the day and season (contextual drivers). To the authors' knowledge, these are the first models of window opening and closing behaviour developed for UK residential buildings. The work reported in this paper suggests that occupants' main bedroom window operation is influenced by a range of physical environmental (i.e. indoor and outdoor air temperature and relative humidity, wind speed, solar radiation and rainfall) and contextual variables (i.e. time of day and season). In addition, the effects of the physical environmental variables were observed to vary in relation to the contextual factors. The models provided in this work can be used to calculate the probability that the main bedroom window will be opened or closed in the next 10 min. These models could be used in building performance simulation applications to improve the inputs for occupants' window opening and closing behaviour and thus the predictions of energy use and indoor environmental conditions of residential buildings. (C) 2017 Elsevier Ltd. All rights reserved.
机译:本文根据一年中在英国十个住宅中收集的测量结果,介绍了居住者主卧室窗户操作的随机模型的发展。该研究使用多元逻辑回归来根据室内和室外环境因素(物理环境驱动因素)并根据一天中的时间和季节(上下文驱动因素)来了解打开和关闭窗户的可能性。据作者所知,这是为英国住宅建筑开发的首个窗户开启和关闭行为模型。本文报道的工作表明,居住者的主卧室窗户操作受到一系列物理环境(即室内和室外空气温度和相对湿度,风速,太阳辐射和降雨)和上下文变量(即一天中的时间和季节)。此外,观察到物理环境变量的影响相对于上下文因素也有所不同。这项工作中提供的模型可以用来计算卧室主窗户在接下来的10分钟内打开或关闭的可能性。这些模型可用于建筑性能模拟应用中,以改善居住者的窗户打开和关闭行为的输入,从而改善住宅建筑的能耗和室内环境条件的预测。 (C)2017 Elsevier Ltd.保留所有权利。

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