Indoor occupancy estimation using carbon dioxide concentration and neural network with random weights
This study presents the indoor occupancy estimation using carbon dioxide concentration and neural network with random weights (NNRW). The utilization of carbon dioxide concentration is as an alternative to overcome the limitation of existing techniques, such as dependency to favourable lighting cond...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/92705/1/MohdIbrahimShapiai2020_IndoorOccupancyEstimationUsingCarbonDioxide.pdf http://eprints.utm.my/id/eprint/92705/ http://dx.doi.org/10.1088/1757-899X/769/1/012011 |
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| Summary: | This study presents the indoor occupancy estimation using carbon dioxide concentration and neural network with random weights (NNRW). The utilization of carbon dioxide concentration is as an alternative to overcome the limitation of existing techniques, such as dependency to favourable lighting condition and camera position. Whereas, NNRW provides a generalized and fast learning speed classification. In this study, MH-Z19 sensor is used to acquire carbon dioxide concentration and the NNRW is a multiclass estimation method. The numbers of the occupants are divided into three different classes, which are 15 occupants, 30 occupant and 50 occupant classes. Result indicates that the NNRW classifier has obtained training and testing accuracy, about 100 percent and 52 percent, respectively. |
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