HVAC | ARTIFICIAL INTELLIGENCE and recognition of occupants activities within office building spaces. The proposed approach is detailed in Figure 1. The first stage focuses on developing and deploying a deeplearning model for real-time detection and recognition of occupancy behaviour within a building space. This is configured and trained using deep-learning techniques, and then validated before being used in an AI-powered camera. Compared with shallow-learning methods, deeplearning techniques can lead to better performance in terms of detection and recognition of various objects. Images of occupants activities including napping, sitting, standing and walking are collected, manually labelled, and used as a training dataset for the model. A second model is also developed, focusing on the detection and recognition of the opening and closing of windows by occupants. Specialist software was used for training the models. An initial experiment was conducted within an office environment to test the capabilities and accuracy of the proposed approach. Results showed an average detection of 92% for occupancy activities and 78% for window status. The second stage focuses on the formation of a profile, based on the detected and recognised occupancy number and activities; this is also called the deep-learning influenced profile (DLIP). This corresponds to each of the detected occupancy activities, which was coupled with the heat-emission rates of occupant-performing activities within an office (CIBSE Guide A). For the windowdetection model, the DLIP was based on the window conditions. Using collected information, the building energy management system can adjust the heating, ventilation and air conditioning systems automatically, to meet actual demands of the spaces in real time. Compared with shallow-learning methods, deep-learning techniques can lead to better performance in terms of detection and recognition of various objects Figure 2 shows a process of DLIP formation for the live detection within a selected office space. The images are only for visualisation purposes. In practice, the current approach will output heat-emission profiles, not actual occupancy information. To assess its feasibility and analyse its potential impact on building energy use, simulations were carried out to perform energy modelling of the case-study building. Based on initial application of the occupancy model within a standard office space, both the static and DLIP were assigned. These were used to assess the potential energy savings that can be achieved. Results suggest use of the DLIP can prevent overestimation of occupancy heating gains by up to an average of 35%. Furthermore, the high detection accuracy resulted in an average difference of only 2.3% between DLIP results and the actual observation profile. Similarly, results achieved from the application of the window-detection model with building energy simulation suggests an effective solution for monitoring windows, especially when they are left open unintentionally. Future research will study the system in other indoor environments and look at factors that have an effect on detection accuracy, such as the position of cameras and the room environmental conditions. CJ The paper Energy management and optimisation of HVAC systems using a deep- learning approach was voted most significant contribution to the art and science of building services engineering at the symposium. PAIGE WENBIN TIEN is a PhD researcher in the Buildings, Energy and Environment Research Group at the Department of Architecture and Built Environment, University of Nottingham. The project is supported by the Engineering and Physical Sciences Research Council. open 92% walking 97% open 94% none 88% sitting 99% sitting 99% 1. Visionbased camera detection and recognition Window A (top left) B (top right) C (bottom left) D (bottom right) Walking (145W) Standing (130W) Sitting (110W) None (0W) Open 3 Count 2. Deeplearninginfluenced profile formation 4 2 1 Closed 0 Time (s) Time (s) 3. HVAC systems control HVAC adjustments Heating, cooling, ventilation, air conditioning Process of deep learning and live detection in an office space 50 November 2020 www.cibsejournal.com CIBSE Nov 2020 pp49-50 HVAC deep learning.indd 50 23/10/2020 16:33