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A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Author

Listed:
  • Paige Wenbin Tien

    (Department of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UK)

  • Shuangyu Wei

    (Department of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UK)

  • John Calautit

    (Department of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UK)

Abstract

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

Suggested Citation

  • Paige Wenbin Tien & Shuangyu Wei & John Calautit, 2020. "A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand," Energies, MDPI, vol. 14(1), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:156-:d:470619
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    References listed on IDEAS

    as
    1. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    2. Jin Dong & Christopher Winstead & James Nutaro & Teja Kuruganti, 2018. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings," Energies, MDPI, vol. 11(9), pages 1-20, September.
    3. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    4. Congedo, Paolo Maria & Baglivo, Cristina & D'Agostino, Delia & Zacà, Ilaria, 2015. "Cost-optimal design for nearly zero energy office buildings located in warm climates," Energy, Elsevier, vol. 91(C), pages 967-982.
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    Cited by:

    1. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
    2. Yoon-Soo Shin & Junhee Kim, 2022. "A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space," Sustainability, MDPI, vol. 14(13), pages 1-13, June.
    3. Fernando Cassola & Leonel Morgado & António Coelho & Hugo Paredes & António Barbosa & Helga Tavares & Filipe Soares, 2022. "Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption," Energies, MDPI, vol. 15(12), pages 1-21, June.

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