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Linking building energy consumption with occupants' energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)

机译:将建筑能耗与商业建筑中居民的能耗行为联系起来:非侵入式居民负荷监测(NIOLM)

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Occupants' energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants' energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data-captured from existing Wi-Fi infrastructureswith power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants' energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants' energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings. (C) 2018 Elsevier B.V. All rights reserved.
机译:占用者的能源消耗行为对商业建筑的整体能源消耗有重大影响。因此,理解和干预这些行为为商业建筑的节能提供了重要的机会。当前的行为修改方法依赖于可用的特定于乘员的能耗数据,但是捕获此类数据通常很昂贵。解决这一挑战的一种可能的解决方案是将能耗与商业建筑中个人居住者的能源使用行为联系起来。在这种情况下,本研究提出了一种非侵入式乘员负载监控(NIOLM)方法,该方法将从现有Wi-Fi基础设施捕获的乘感数据与总建筑能耗数据中的功率变化结合在一起,从而将建筑数据分解为个人。本文介绍了两个案例研究,这些案例研究了使用NIOLM方法识别特定于乘员的能耗信息的可行性。使用NIOLM在四个月的时间内跟踪了11个居住者的能源使用行为,得出的平均F值为0.778,准确度为0.955。因此,案例研究表明,NIOLM通过利用商业建筑中现有的高分辨率计量设备和Wi-Fi网络基础设施,以最小的成本成功跟踪了个人乘员的能源消耗行为。 (C)2018 Elsevier B.V.保留所有权利。

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