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Feature selection and Gaussian Process regression for personalized thermal comfort prediction

机译：特征选择和高斯过程回归，用于个性化的热舒适性预测

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摘要

User comfort is one of the main goals for the design of heating, ventilation and air conditioning systems. Therefore, well predicting comfort models are essential for keeping a room in comfortable conditions and furthermore enabling control approaches in accordance with occupants' preferences. Most comfort models target the representation of an average sensation of all occupants. However, in large office spaces, the thermal sensation can vary significantly between the occupants and averaged comfort predictions do not necessarily represent the individual preferences. To allow for customized thermal conditions, user feedback is collected in daily working routine and used for the development of a personalized comfort prediction model. Furthermore, the presented approach takes into account the effect of increased air movement to enable customized conditions. Individual comfort models are derived for each user based on a hybrid approach: The widely used predicted mean vote (PMV) is used as a starting point and evaluated for the given voting data. Based on this analysis, the proposed comfort model is composed of a polynomial basis function extended by a Gaussian Process regression model to capture the complex and highly subjective relations between room conditions and thermal perception. An analysis of the significance of all measured variables is performed to reduce the model's complexity by selecting the most impacting parameters. This approach allows for a 74% higher individual prediction accuracy compared to the standard PMV calculation.
机译：用户舒适度是供暖，通风和空调系统设计的主要目标之一。因此，良好预测的舒适度模型对于保持房间处于舒适状态并进一步根据乘员的喜好启用控制方法至关重要。大多数舒适度模型的目标是代表所有乘员的平均感觉。但是，在大型办公空间中，乘员之间的热感可能会有很大差异，并且平均舒适度预测不一定代表个人的喜好。为了允许定制的热工条件，在日常工作中收集用户反馈，并将其用于个性化舒适度预测模型的开发。此外，提出的方法考虑到增加空气流动以实现定制条件的影响。基于混合方法为每个用户得出单独的舒适度模型：广泛使用的预测平均投票（PMV）作为起点，并针对给定的投票数据进行评估。基于此分析，提出的舒适度模型由多项式基函数组成，该函数由高斯过程回归模型扩展，以捕获房间条件与热感之间的复杂且高度主观的关系。通过选择影响最大的参数，对所有测量变量的重要性进行了分析，以降低模型的复杂性。与标准PMV计算相比，此方法可将个人预测准确性提高74％。

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