A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand
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 m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/1/156 |
| _version_ | 1797543074310651904 |
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| author | Paige Wenbin Tien Shuangyu Wei John Calautit |
| author_facet | Paige Wenbin Tien Shuangyu Wei John Calautit |
| author_sort | Paige Wenbin Tien |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:39:32Z |
| format | Article |
| id | doaj.art-3ef96fc779284e3c8572d37cf2e4f008 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:39:32Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3ef96fc779284e3c8572d37cf2e4f0082023-11-21T03:07:50ZengMDPI AGEnergies1996-10732020-12-0114115610.3390/en14010156A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy DemandPaige Wenbin Tien0Shuangyu Wei1John Calautit2Department of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UKDepartment of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UKDepartment of Arch and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UKBecause 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.https://www.mdpi.com/1996-1073/14/1/156built environmentcomputer visiondeep learningequipmentheat gainsHVAC system |
| spellingShingle | Paige Wenbin Tien Shuangyu Wei John Calautit A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand Energies built environment computer vision deep learning equipment heat gains HVAC system |
| title | A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand |
| title_full | A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand |
| title_fullStr | A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand |
| title_full_unstemmed | A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand |
| title_short | A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand |
| title_sort | computer vision based occupancy and equipment usage detection approach for reducing building energy demand |
| topic | built environment computer vision deep learning equipment heat gains HVAC system |
| url | https://www.mdpi.com/1996-1073/14/1/156 |
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