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Promoting Energy Efficiency of HVAC Operation in Large Office Spaces with a Wi-Fi Probe enabled Markov Time Window Occupancy Detection Approach

机译:使用Wi-Fi探测器启用大型办公空间中HVAC操作的能效,使MARKOV TIME窗口占用检测方法

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In recent years, demand-based control in HVAC systems have achieved a great amount energy saving by providing precise services in according to actual demand. As the premise of determining actual demand, occupancy information has a significant impact on building heating, cooling and ventilation in building control and energy auditing. In this paper, a Wi-Fi probe based occupancy detection method is applied to detect the occupancy in typical office buildings. Given the Wi-Fi coverage and smart devices are widely used in modern buildings, Wi-Fi probe requires no initial investment and could scan the Internet connection request and response of occupants. Previous studies suggest time-series and stochastic characteristics of occupancy information, this research proposed a Time-Window based Markov Chain (TWMC) model to detect occupancy. An on-site experiment was conducted in this study to validate the proposed method. The results report an accuracy of over 80% (x-accuracy when x equals 4). Compared to actual occupancy profile, the proposed model shows over 88% of supply air amount reduction in energy simulation with an absolute deviation less than 20%. By integrating the proposed TWMC model based on Wi-Fi probe and demand-based control system, the energy consumption of HVAC system could be significantly reduced.
机译:近年来,通过根据实际需求提供精确的服务,HVAC系统中基于需求的控制已经实现了大量节能。作为确定实际需求的前提,占用信息对建筑控制和能源审计方面的建筑加热,冷却和通风产生了重大影响。本文采用了基于Wi-Fi探针的占用检测方法来检测典型办公楼中的占用。鉴于Wi-Fi覆盖范围和智能设备广泛用于现代建筑物,Wi-Fi探针不需要初始投资,并且可以扫描互联网连接请求和乘员的响应。以前的研究表明了占用信息的时间系列和随机特征,这项研究提出了一种基于时间窗口的马尔可夫链(TWMC)模型来检测占用。本研究进行了现场实验,以验证提出的方法。结果报告了超过80%的准确性(当x等于4时x精度)。与实际占用概况相比,所提出的模型显示出超过88%的供应空气量减少能量模拟,绝对偏差小于20%。通过基于Wi-Fi探测和基于需求的控制系统集成所提出的TWMC模型,可以显着降低HVAC系统的能耗。

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