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Data-driven occupant actions prediction to achieve an intelligent building
Building Research & Information ( IF 3.9 ) Pub Date : 2019-11-25 , DOI: 10.1080/09613218.2019.1692648
Pedro F. Pereira 1 , Nuno M. M. Ramos 1 , M. Lurdes Simões 1
Affiliation  

ABSTRACT An intelligent building has to know the specificities of the occupants and determine their drivers to perform actions so that it can optimize the building operation. Five windows of different rooms of the same dwelling were analysed in-depth to understand the specificities and variations of occupants’ behaviour. Logistic regressions were used as a machine learning method to predict occupants’ actions. The windows opening prediction models were formulated by taking into account continuous and categorical variables. An evaluation of the required data length that allows obtaining the prediction models with results identical to those obtained with the complete year was performed. It was concluded that the best option was to use at least 15 days in summer and 15 days in winter to have a reliable prediction for the full year. The model constructed for each window did not show good prediction success when applied in another room of the same dwelling. This study shows that the specificity of humans needs do not allow a generalization of their behaviours in the built environment. Thus, it is necessary to adapt the algorithms of the building automation systems through data-driven machine learning techniques.

中文翻译:

数据驱动的乘员动作预测,实现智能建筑

摘要 智能建筑必须了解居住者的特殊性并确定他们的驱动程序来执行操作,以便优化建筑运行。深入分析了同一住宅不同房间的五个窗户,以了解居住者行为的特殊性和变化。逻辑回归被用作机器学习方法来预测乘员的行为。窗户打开预测模型是通过考虑连续变量和分类变量来制定的。对所需数据长度进行了评估,以便获得与全年获得的结果相同的预测模型。得出的结论是,最好的选择是至少使用夏季 15 天和冬季 15 天,以便对全年进行可靠的预测。当应用于同一住宅的另一个房间时,为每个窗口构建的模型没有显示出良好的预测成功。这项研究表明,人类需求的特殊性不允许他们在建筑环境中的行为泛化。因此,有必要通过数据驱动的机器学习技术来调整楼宇自动化系统的算法。
更新日期:2019-11-25
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