Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network
Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network
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DOI：
10.1080/19401493.2011.577810
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发表时间：
2011-01-01
影响因子：
2.5
通讯作者：
Lam, Khee Poh
中科院分区：
文献类型：
--
作者：
Dong, Bing;Lam, Khee Poh
居住者存在的检测已经广泛地用于建筑环境中的应用，例如需求控制的通风和安全。然而，辨别房间中的实际人数的能力超出了大多数当前传感技术的范围。为了解决这一问题，在罗伯特L。卡内基梅隆大学的Preger智能工作场所（IW）。结果表明，测量的环境条件和占用状态之间有显着的相关性。结果表明，在测试期间，平均83%的准确率的占用人数检测高斯混合模型基于隐马尔可夫模型。为了说明基于空间中的占用者行为检测（即占用的数量和持续时间）的随之而来的能量影响，创建了具有假设的标准变风量（VAV）系统的IW的EnergyPlus模型。进行模拟，以比较根据ASHRAE 90.1基本情况与预测的占用行为之间的规定的占用时间表的能源消耗的后果。结果表明，在保持室内热舒适性的前提下，智能窗可实现18.5%的节能效果。
Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security. However, the ability to discern the actual number of people in a room is beyond the scope of most current sensing techniques. To address this issue, a complex environmental sensor network is deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University. The results indicate that there are significant correlations between measured environmental conditions and occupancy status. It is shown that an average of 83% accuracy on the occupancy number detection was achieved by Gaussian Mixture Model based Hidden Markov Models during testing periods. To illustrate the consequent energy impact based on the occupant behaviour detection (i.e. number and duration of occupancy) in the space, an EnergyPlus model of the IW with an assumed standard variable air volume (VAV) system is created. Simulations are conducted to compare the energy consumption consequences between a prescribed occupancy schedule according to the ASHRAE 90.1 base case with the predicted occupancy behaviour. The results show that energy saving of 18.5% can be achieved in the IW while maintaining indoor thermal comfort.