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Method for room occupancy detection based on trajectory of indoor climate sensor data

机译：基于室内气候传感器数据轨迹的房间占用检测方法

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摘要

Significant energy savings can be achieved by operating heating, ventilation and air conditioning controllers using a feedback from sensor-based occupancy detection methods. This paper presents a novel plug-and-play occupancy detection method based on the trajectory of various indoor climate sensor data. Sensor data obtained from two different building zones was used to test the efficacy of the method. For a simple test room, occupancy detection based on CO2 sensor data had the best performance with a mean absolute error (MAE) of 2%, closely followed by PIR and volatile organic compound (VOC) with a MAE of 3% and 4%, respectively. For a real dorm apartment with three rooms, but only one data logger, the best performance was found when PIR was used to determine when the apartment went from unoccupied to occupied and either VOC or CO2 sensor data was used to determine when the apartment went from occupied to unoccupied (MAE of 22% and 26%, respectively). Compared to more complex detection methods that require detailed information about the physical conditions of rooms or extensive training data sets, the proposed plug-and-play method that employs simple trajectory of CO2, PIR and VOC sensor data resulted in similar occupancy detection accuracies. (C) 2017 Elsevier Ltd. All rights reserved.
机译：通过使用基于传感器的占用检测方法的反馈，通过操作供暖，通风和空调控制器，可以节省大量能源。本文提出了一种基于各种室内气候传感器数据轨迹的即插即用占用检测方法。从两个不同的建筑区域获得的传感器数据用于测试该方法的有效性。对于一个简单的测试室，基于CO2传感器数据的占用检测性能最好，平均绝对误差（MAE）为2％，紧随其后的是PIR和挥发性有机化合物（VOC），MAE分别为3％和4％，分别。对于只有三个数据记录器但只有一个数据记录器的真实宿舍公寓，当使用PIR来确定公寓何时从空置变成有人住，并且使用VOC或CO2传感器数据来确定公寓何时从空置时，发现了最佳性能。占空屋（MAE分别为22％和26％）。与需要有关房间的物理状况或大量训练数据集的详细信息的更复杂的检测方法相比，所建议的即插即用方法采用了CO2，PIR和VOC传感器数据的简单轨迹，因此具有类似的占用检测精度。 （C）2017 Elsevier Ltd.保留所有权利。

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