As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions.

Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

随着外部温度和设备内部收益的上升，办公楼的冷却需求和问题可能会增加。需求驱动控制等解决方案可以通过协调实时供暖、通风和空调 (HVAC) 使用来满足空调空间的要求，从而帮助最大限度地减少能源消耗并保持建筑物的热舒适性。本研究介绍了一种使用深度学习方法进行需求驱动控制的实时设备使用检测和识别方法。Faster R-CNN 模型经过训练并部署到相机上。该模型的性能通过不同的评估指标进行评估。根据初步现场实验结果，检测准确率为76.21%。为了研究所提出的方法对建筑供暖和制冷能源需求的影响，对案例研究建筑进行了建模和模拟。结果表明，与基于设定条件的静态或“固定”计划相比，基于深度学习的方法预测的内部热量增益最多可降低 35.95%。

实际应用：由于建筑空间中的电器和设备有助于内部热量增益，它们的使用会影响建筑能源需求和室内热环境。将设备使用情况与居住者在空间中的存在联系起来可能不完全准确，并可能导致高估或低估热排放，尤其是当空间无人居住且设备已通电或相反时。这种方法可以与 HVAC 系统的需求驱动控制相结合，从而可以最大限度地减少不必要的建筑能耗，同时使用计算机视觉和深度学习检测和识别方法保持舒适的室内环境。