An opportunistic activity-sensing approach to save energy in office buildings
An opportunistic activity-sensing approach to save energy in office buildings
复制标题
一种节省办公楼能源的机会活动感应方法
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
O. Amft
中科院分区：
文献类型：
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作者：
M. Milenkovic;O. Amft
在这项工作中，我们识别了与使用传感器对电器和建筑系统进行能源相关控制的上班族活动，这些传感器通常安装在新建或翻新的办公大楼中。我们考虑了与办公桌相关的活动和办公室里的人数，并将其组织成办公桌和房间单元。识别采用有限状态机(FSM)和概率分层隐马尔可夫模型(LHMM)。
我们在一个真实的生活实验室办公室中评估了我们的方法，包括三个私人和多人办公室。作为示例设备，我们使用了基于EnOcean平台的不同天花板安装的PIR传感器和插电式功率计。在每个办公室至少五天的研究数据中，包括参考传感器数据和居住者注释，我们证实可以使用这些传感器识别活动。对于计算机和桌面工作，总体识别准确率达到95%。人数统计估计为87%，表现最好的两个办公室的人数为78%。我们还给出了建筑仿真结果，比较了不同的控制策略。与现代的BEMS相比，我们的结果显示，基于识别的办公桌活动和估计的人数，控制分别可以节省21.9%和19.5%的电能。这些结果证实了基于活动感知的建筑能源管理的相关性。
In this work, we recognised office worker activities that are relevant for energy-related control of appliances and building systems using sensors that are commonly installed in new or refurbished office buildings. We considered desk-related activities and people count in office rooms, structured into desk- and room-cells. Recognition was performed using finite state machines (FSMs) and probabilistic layered hidden Markov models (LHMMs).
We evaluated our approach in a real living-lab office, including three private and multi-person office rooms. As example devices, we used different ceiling-mounted PIR sensors based on the EnOcean platform and plug-in power meters. In at least five days of study data per office room, including reference sensor data and occupant annotations, we confirmed that activities can be recognised using these sensors. For computer and desk work, an overall recognition accuracy of 95% was achieved. People count was estimated at 87% and 78% for the best-performing two office rooms. We furthermore present building simulation results that compare different control strategies. Compared to modern BEMS, our results show that 21.9% and 19.5% of electrical energy can be saved for controls based on recognised desk activity and estimated people count, respectively. These results confirm the relevance of building energy management based on activity sensing.