基于自编码器神经网络的室内环境数据时间序列重构
Indoor environment data time-series reconstruction using autoencoder neural networks
摘要：
随着建筑物中安装的仪表数量的增加，可以用于开发数据驱动模型以支持和优化建筑物操作的数据时间序列的数量也在增加。然而，建立数据集的特点往往是错误和缺失值，这是考虑，最近的研究，在所提出的模型的性能的主要限制因素。出于解决建筑运营中缺失数据问题的需要，本文提出了一种数据驱动的方法来填补这些空白。在这项研究中，三个不同的自动编码器神经网络的训练，以重建缺失的短期室内环境数据时间序列在德国亚琛的办公楼收集的数据集。这包括在2014年至2017年期间对84个不同房间进行为期四年的监测活动。该模型适用于从房间自动化获得的不同时间序列，例如室内空气温度、相对湿度和$CO_{2}$数据流。结果表明，所提出的方法优于经典的数值方法，它们导致重建相应的变量，平均RMSE分别为0.42 °C，1.30%和78.41 ppm。
英文摘要：
As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing short-term indoor environment data time-series in a data set collected in an office building in Aachen, Germany. This consisted of a four year-long monitoring campaign in and between the years 2014 and 2017, of 84 different rooms. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and $CO_{2}$ data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 °C, 1.30 % and 78.41 ppm, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
