Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SDEWES Centre
2021-09-01
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| Series: | Journal of Sustainable Development of Energy, Water and Environment Systems |
| Subjects: | |
| Online Access: |
http://www.sdewes.org/jsdewes/pid8.0378
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| _version_ | 1818345145948438528 |
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| author | Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood |
| author_facet | Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood |
| author_sort | Paige Wenbin Tien |
| collection | DOAJ |
| description | The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems. |
| first_indexed | 2024-12-13T16:57:44Z |
| format | Article |
| id | doaj.art-666ffb3b601a4ba6b268a36b46d25813 |
| institution | Directory Open Access Journal |
| issn | 1848-9257 |
| language | English |
| last_indexed | 2024-12-13T16:57:44Z |
| publishDate | 2021-09-01 |
| publisher | SDEWES Centre |
| record_format | Article |
| series | Journal of Sustainable Development of Energy, Water and Environment Systems |
| spelling | doaj.art-666ffb3b601a4ba6b268a36b46d258132022-12-21T23:37:52ZengSDEWES CentreJournal of Sustainable Development of Energy, Water and Environment Systems1848-92572021-09-019313110.13044/j.sdewes.d8.03781080378Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demandPaige Wenbin Tien0Shuangyu Wei1John Calautit2Jo Darkwa3Christopher Wood4 Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems. http://www.sdewes.org/jsdewes/pid8.0378 artificial intelligencedeep learningenergy managementoccupancy detectionactivity detectionhvac system. |
| spellingShingle | Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand Journal of Sustainable Development of Energy, Water and Environment Systems artificial intelligence deep learning energy management occupancy detection activity detection hvac system. |
| title | Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| title_full | Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| title_fullStr | Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| title_full_unstemmed | Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| title_short | Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| title_sort | occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand |
| topic | artificial intelligence deep learning energy management occupancy detection activity detection hvac system. |
| url |
http://www.sdewes.org/jsdewes/pid8.0378
|
| work_keys_str_mv | AT paigewenbintien occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT shuangyuwei occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT johncalautit occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT jodarkwa occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT christopherwood occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand |