论文标题
通过合成环境数据检测建筑物占用
Towards the Detection of Building Occupancy with Synthetic Environmental Data
论文作者
论文摘要
有关室内占用的信息对于许多与建筑有关的任务（例如建筑自动化或能量性能模拟）至关重要。当前的占用检测文献集中在数据驱动的方法上，但主要基于几个房间的小案例研究。在实践中，收集每个感兴趣的房间的室内数据的必要性阻碍了机器学习的适用性，尤其是数据密集型深度学习方法。为了从较少的数据中得出准确的预测，我们建议从合成数据中转移知识传输。在本文中，我们对办公室里的CO $ _2 $传感器的数据进行了实验，并从模拟中获得了其他合成数据。我们的贡献包括（a）在随机乘员行为下的CO $ _2 $动力学的模拟方法，（b）从模拟CO $ _2 $数据中的知识转移的概念验证，以及（c）未来研究含义的概述。从我们的结果中，我们可以得出结论，转移方法可以有效地减少模型培训所需的数据量。
Information about room-level occupancy is crucial to many building-related tasks, such as building automation or energy performance simulation. Current occupancy detection literature focuses on data-driven methods, but is mostly based on small case studies with few rooms. The necessity to collect room-specific data for each room of interest impedes applicability of machine learning, especially data-intensive deep learning approaches, in practice. To derive accurate predictions from less data, we suggest knowledge transfer from synthetic data. In this paper, we conduct an experiment with data from a CO$_2$ sensor in an office room, and additional synthetic data obtained from a simulation. Our contribution includes (a) a simulation method for CO$_2$ dynamics under randomized occupant behavior, (b) a proof of concept for knowledge transfer from simulated CO$_2$ data, and (c) an outline of future research implications. From our results, we can conclude that the transfer approach can effectively reduce the required amount of data for model training.
