International Centre for Indoor Environment and Energy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
Int J Environ Res Public Health. 2019 Nov 7;16(22):4349. doi: 10.3390/ijerph16224349.
Non-optimal air temperatures can have serious consequences for human health and productivity. As the climate changes, heatwaves and cold streaks have become more frequent and intense. The ClimApp project aims to develop a smartphone App that provides individualised advice to cope with thermal stress outdoors and indoors. This paper presents a method to predict indoor air temperature to evaluate thermal indoor environments. Two types of input data were used to set up a predictive model: weather data obtained from online weather services and general building attributes to be provided by App users. The method provides discrete predictions of temperature through a decision tree classification algorithm. The data used to train and test the algorithm was obtained from field measurements in seven Danish households and from building simulations considering three different climate regions, ranging from temperate to hot and humid. The results show that the method had an accuracy of 92% (F1-score) when predicting temperatures under previously known conditions (e.g., same household, occupants and climate). However, the performance decreased to 30% under different climate conditions. The approach had the highest performance when predicting the most commonly observed indoor temperatures. The findings suggest that it is possible to develop a straightforward and fairly accurate method for indoor temperature estimation grounded on weather data and simple building attributes.
非最佳的空气温度可能对人类健康和生产力产生严重影响。随着气候变化，热浪和寒冷天气变得更加频繁和强烈。ClimApp 项目旨在开发一款智能手机应用程序，为户外和户内的热应激提供个性化建议。本文提出了一种预测室内空气温度的方法，以评估室内热环境。该方法使用两种类型的输入数据来建立预测模型：从在线气象服务获取的天气数据和应用程序用户提供的一般建筑属性。该方法通过决策树分类算法提供温度的离散预测。用于训练和测试算法的数据是从丹麦七个家庭的现场测量以及考虑三个不同气候区域（从温和到炎热和潮湿）的建筑模拟中获得的。结果表明，当预测先前已知条件下（例如，同一家庭、居住者和气候）的温度时，该方法的准确率为 92%（F1 分数）。然而，在不同的气候条件下，性能下降到 30%。当预测最常见的室内温度时，该方法具有最高的性能。研究结果表明，基于天气数据和简单的建筑属性，开发一种简单且相当准确的室内温度估计方法是可行的。
