Abstract

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

中文翻译：

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通过机器学习算法预测服装隔热性：比较分析和实用方法

摘要

由于室内服装隔热是热舒适模型中的关键要素，本研究的目的是提出一种预测它的方法，这可以帮助建筑物的居住者提出有关其整体的建议。为此，对输入变量进行了系统分析，并开发和比较了 13 种回归和 12 种分类机器学习算法。结果基于 3352 份问卷的数据和来自西班牙混合模式办公楼实地研究的 21 个输入变量。早上 6 点的室外温度、室内空气温度、室内相对湿度、舒适温度和性别是预测服装隔热性最相关的特征。在比较机器学习算法时，基于决策树的算法和 Boosting 技术实现了最佳性能。所提出的模型提供了一种预测服装隔热水平的有效方法，其应用将需要优化热舒适性和能源效率。