基于视觉的人类活动识别,减少建筑能源需求

IF 1.5 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
P. Tien, S. Wei, J. Calautit, J. Darkwa, Christopher Wood
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引用次数: 6

摘要

楼宇内的使用行为会影响能源表现及暖气、通风及空调系统的运作。为了确保建筑运行得到优化,开发能够监控室内空间利用率并提供居住者实际热舒适要求的解决方案至关重要。本研究分析了基于视觉的深度学习方法在建筑物中人类活动检测和识别的应用。采用卷积神经网络对占用活动进行检测和分类。该模型被部署到一个能够实时检测的相机上,平均检测精度为98.65%。收集了执行每项选定活动的住户人数的数据,并生成了受深度学习影响的概况。建筑能源模拟和各种基于场景的案例被用来评估这种方法对建筑能源需求的影响,并为所提出的检测方法如何使供暖、通风和空调系统响应占用的动态变化提供见解。结果表明,深度学习方法可以减少对入住热增益的过高或过低估计。设想这种方法可以结合供暖、通风和空调控制,根据建筑空间的实际需求调整设定值,从而提供更舒适的环境,并最大限度地减少不必要的建筑能源负荷。实际应用使用行为已被确定为影响建筑和供暖、通风和空调系统能源需求的重要问题。本研究提出了一种基于视觉的深度学习方法,用于实时捕获、检测和识别办公空间环境中的占用模式和活动。对该方法在建筑中的应用进行了初步的建筑能量模拟分析。所提出的方法设想使供暖、通风和空调系统能够适应并根据占用率的动态变化及时做出反应。这里展示的结果显示了这种方法的实用性,它可以与各种建筑空间和环境的供暖、通风和空调系统相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision-based human activity recognition for reducing building energy demand
Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.
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来源期刊
Building Services Engineering Research & Technology
Building Services Engineering Research & Technology 工程技术-结构与建筑技术
CiteScore
4.30
自引率
5.90%
发文量
38
审稿时长
>12 weeks
期刊介绍: Building Services Engineering Research & Technology is one of the foremost, international peer reviewed journals that publishes the highest quality original research relevant to today’s Built Environment. Published in conjunction with CIBSE, this impressive journal reports on the latest research providing you with an invaluable guide to recent developments in the field.
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