Occupancy estimation using environmental parameters
Occupancy information is important for energy efficient operations of Air Conditioning and Mechanical Ventilation (ACMV) systems. To predict the occupancy in a room, environmental sensors are increasingly used as cost effective and non-intrusive ways to obtain the occupancy information. This requir...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158096 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Occupancy information is important for energy efficient operations of Air Conditioning and Mechanical Ventilation (ACMV) systems. To predict the occupancy in a room, environmental sensors are increasingly used as cost effective and non-intrusive ways to obtain the occupancy information. This requires extraction of environmental parameters such as carbon dioxide, temperature and humidity which are used to provide a non-intrusive representation of occupancy in the room. These parameters are extracted by a data collection system consisting of a temperature sensor, humidity sensor and a gas sensor. This paper shows prediction using Logistic Regression to predict the occupancy of the room whether it is occupied or not occupied. Experiments are done in a small room in a building with few occupants. |
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