Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets
Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets
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DOI：
10.1016/j.buildenv.2019.04.015
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发表时间：
2019-06-15
影响因子：
7.4
通讯作者：
Polak, John
中科院分区：
文献类型：
--
作者：
Howard, Bianca;Acha, Salvador;Polak, John
占用计数，即空间或建筑中的人员数量，正在成为建模、预测和最小化运营能耗的越来越重要的衡量标准。已经提出了显式的、基于硬件的占用计数器，但由于系统实施的成本和侵入性，广泛采用受到限制。作为一种替代方法，研究人员建议使用现有信息和通信技术(ICT)系统的数据来推断入住率。在报告的工作中，从一座中型写字楼的ICT系统收集了三种不同的数据流，分别是安全访问数据、无线连接数据和计算机活动数据，并在59个工作日内与商业可用入住率计数器的计数进行了比较。将信通技术系统的入住率计数与有无校准的商业计数器进行比较，以确定数据集衡量入住率的能力。作为信通技术数据集的校准技术，对各种转换进行了探讨。使用24、48和120小时的训练集来确定需要安装多长时间的外部校准系统。分析发现，需要校准才能提供准确的计数。虽然每个信通技术数据集都提供类似的大小和时间序列行为，但将所有三个数据流合并到一个带有1周训练数据的两层神经网络中，可针对5个业绩指标提供最准确的估计。虽然1周的数据提供了最好的结果，但24小时足以产生类似的性能水平。
Occupancy count, i.e., the number of people in a space or building, is becoming an increasingly important measurement to model, predict, and minimize operational energy consumption. Explicit, hardware-based, occupancy counters have been proposed but wide scale adoption is limited due to the cost and invasiveness of system implementation. As an alternative approach, researchers propose using data from existing information and communication technology (ICT) systems to infer occupancy counts.In the reported work, three different data streams, security access data, wireless connectivity data, and computer activity data, from ICT systems in a medium sized office building were collected and compared to the counts of a commercially available occupancy counter over 59 working days. The occupancy counts from the ICT systems are compared to the commercial counter with and without calibration to determine the ability of the data sets to measure occupancy. Various transformations were explored as calibration techniques for the ICT data sets. Training sets of 24, 48, and 120 hours were employed to determine how long an external calibration system would need to be installed.The analysis found that calibration is required to provide accurate counts. While each ICT data set provides similar magnitudes and time series behavior, incorporating all three data streams in a two layer neural network with 1 week of training data provides the most accurate estimates against 5 performance metrics. Whilst 1 week of data provides the best results, 24 hours is sufficient to develop similar levels of performance.