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.

中文翻译：

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基于视觉的人类活动识别减少建筑能源需求

建筑物的入住行为会影响能源性能以及供暖、通风和空调系统的运行。为确保优化建筑运营，开发能够监控室内空间利用率并满足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中的应用。使用卷积神经网络来检测和分类占用活动。该模型被部署到启用实时检测的摄像头上，平均检测准确率为 98.65%。收集了有关执行每个选定活动的乘员数量的数据，并生成了受深度学习影响的配置文件。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，并深入了解所提出的检测方法如何使供暖、通风和空调系统能够响应占用的动态变化。结果表明，深度学习方法可以减少对占用热增益的高估或低估。设想该方法可以与供暖、通风和空调控制相结合，根据建筑空间的实际要求调整设定值，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

入住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来实时捕捉、检测和识别办公空间环境中的占用模式和活动。对这种方法在建筑物内的应用进行了初步的建筑能源模拟分析。提议的方法旨在使供暖、通风和空调系统能够适应并根据占用的动态变化做出及时响应。此处展示的结果显示了这种方法的实用性，该方法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。