Non-invasive occupancy estimation and space utilization in smart buildings: Leveraging machine learning with PIR sensors and booking data

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Azad Shokrollahi , Fredrik Karlsson , Reza Malekian , Jan A. Persson , Arezoo Sarkheyli-Hägele
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引用次数: 0

Abstract

Occupancy estimation in smart buildings is essential for optimizing resource usage and enhancing operational efficiency. Existing estimation methods predominantly rely on cameras or advanced sensor fusion techniques, which, while accurate, are often expensive, invasive, and raise privacy concerns. Additionally, these approaches frequently require extra hardware, increasing installation complexity and operational costs. A significant gap in the literature lies in the limited use of existing smart building infrastructure, such as detection systems and booking data, for people counting. This study addresses these limitations by exclusively utilizing two binary PIR sensors (in-door and in-room) and booking data. Since PIR sensors and booking systems are already integrated into most smart building infrastructures, leveraging these existing resources helps reduce costs and simplifies implementation. The primary goal is to estimate the number of people between each in-door sensor trigger using machine learning models by incorporating people counting levels and time thresholds. Among the evaluated machine learning algorithms, the Extra Trees Classifier delivered strong performance, achieving 68.5% accuracy when the estimated occupancy differed from the actual count by at most one person, and 81.56% with a tolerance of two. These results are based on periods when the room was occupied. When both occupied and unoccupied periods were included, the accuracy was 96.10% for ±1 tolerance. Moreover, incorporating booking data enhanced people counting accuracy by 4%. The study also explores the method’s ability to identify underutilization and overutilization by comparing estimated occupancy with booking records and seating capacity, thereby supporting enhanced space management in smart buildings.
智能建筑中的非侵入式占用估计和空间利用:利用PIR传感器和预订数据的机器学习
智能建筑的占用估算对于优化资源利用和提高运行效率至关重要。现有的估计方法主要依赖于相机或先进的传感器融合技术,这些技术虽然准确,但往往昂贵,具有侵入性,并引起隐私问题。此外,这些方法经常需要额外的硬件,增加了安装的复杂性和操作成本。文献中的一个重大空白在于,现有的智能建筑基础设施(如检测系统和预订数据)对人员计数的使用有限。本研究通过专门利用两个二进制PIR传感器(室内和室内)和预订数据来解决这些限制。由于PIR传感器和预订系统已经集成到大多数智能建筑基础设施中,因此利用这些现有资源有助于降低成本并简化实施。主要目标是通过结合人员计数水平和时间阈值,使用机器学习模型估计每次室内传感器触发之间的人数。在评估的机器学习算法中,Extra Trees Classifier提供了强大的性能,当估计入住率与实际人数最多相差一人时,准确率达到68.5%,在误差为两人的情况下,准确率达到81.56%。这些结果是基于房间被占用的时间。当包括已占用期和未占用期时,准确度为96.10%,误差为±1。此外,结合预订数据将人数统计准确率提高了4%。该研究还探讨了该方法通过比较预估入住率与预订记录和座位容量来识别未充分利用和过度利用的能力,从而支持智能建筑中增强的空间管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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