Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand. (15th February 2022)

• Record Type: Journal Article
• Authors: Tien, Paige Wenbin; Wei, Shuangyu; Calautit, John Kaiser; Darkwa, Jo; Wood, Christopher
• Is Part Of: Applied energy. Volume 308(2022)
• Publication Date: 2022-02-15
• Subjects: Deep learning -- Building energy management -- Building ventilation -- Window opening -- Window and occupancy detection -- HVAC systems; AI Artificial Intelligence -- BEMS Building Energy Management Systems -- BES Building Energy Simulation -- CIBSE Chartered Institution of Building Services Engineers -- CNN Convolutional Neural Network -- CO2 Carbon Dioxide -- DL Deep Learning -- DLIP Deep Learning Influenced Profile -- HVAC Heating, Ventilation and Air-Conditioning -- IAQ Indoor Air Quality -- IESVE Integrated Environmental Solutions Virtual Environment -- IoU Intersection over Union -- R-CNN Region-based Convolutional Neural Network -- RFID Radio Frequency Identification -- U U-value (W/m2K) -- UK United Kingdom; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118336
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A vision-based approach was developed to detect occupancy activities and window conditions. A deep learning model was trained and deployed to a camera for multi-object detection. Field testing was carried out by performing real-time detection in a university lecture room. Results showed that the approach could reduce the under or overestimation of heat gains. The impact on heat loss, energy demand and indoor air quality was evaluated using building simulation. Abstract: Occupancy behaviour in buildings can impact the energy performance and operation of heating, ventilation and air-conditioning (HVAC) systems. HVAC, which uses conventional control strategies or "fixed" setpoint schedules, could not adjust to the conditioned spaces' actual requirements, resulting in building spaces being over or under-conditioned. While the unintended opening of windows can lead to substantial heat loss and consequently raises energy consumption. To optimise building operations, it is necessary to employ solutions such as demand-driven controls, which can monitor the utilisation of indoor spaces and provide the actual thermal comfort requirements of occupants. This study presents a novel vision-based deep learning framework for occupancy activity detection and recognition including the manual window operations in buildings. A region-based Convolutional Neural Network (R-CNN) model was trained and deployed to a camera for real-time detection and recognition. Based on the field experiments conducted within a case study University building, overall accuracy of 85.63% was achieved for occupancy activity detection and 92.20% for window operation detection. 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 HVAC systems to respond to dynamic changes within indoor spaces. Results showed that the proposed approach could reduce the over-or under-estimation of occupancy heat gains compared with the use of "fixed" or static profiles. In addition, the approach can help alert building users or managers about windows left open unintentionally, which can reduce unnecessary ventilation heat losses. Furthermore, the approach can also predict the room CO2 concentration and advise occupants about a suitable natural ventilation strategy. The study highlighted the potential of the multi-purpose detection approach, but further development is necessary, including optimisation of the deep learning model, full integration with HVAC controls and further model training and field testing.