Occupancy modelling using data driven models
Indoor occupancy information is key to office and home automation systems. It is used as an input for the control of indoor lighting systems [1] and Heat, Ventilation and Air-conditioning (HVAC) systems [2]. HVAC technology ensures constant supply of good quality air and thermal comfort for occupant...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/140536 |
| _version_ | 1811677786293665792 |
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| author | Lee, Gabriel Hanjie |
| author2 | Soh Yeng Chai |
| author_facet | Soh Yeng Chai Lee, Gabriel Hanjie |
| author_sort | Lee, Gabriel Hanjie |
| collection | NTU |
| description | Indoor occupancy information is key to office and home automation systems. It is used as an input for the control of indoor lighting systems [1] and Heat, Ventilation and Air-conditioning (HVAC) systems [2]. HVAC technology ensures constant supply of good quality air and thermal comfort for occupants to live and work using designed heating, filtration and ventilation systems. As our society steadily progresses towards a sustainable future by reducing ecological footprints, more emphasis and attention has been given to the issue of building energy optimization. Studies have also shown that around one-third of the energy consumed in buildings can be saved using occupancy-based control [3]. As such, a great amount of attention has been given to energy efficiency issues in designing and improving our buildings today.
A conventional way to estimate the occupancy level in a particular room is to employ numerous sensors in order to completely capture the occupancy profile of the entire environment. Data collected from multi-camera videos coupled with pattern recognition technology can accurately estimate the number of indoor occupants, however, these methods require expensive hardware and are not often used due to their intrusive nature which brings privacy concerns. Thus, many non-intrusive and non-terminal-based types of sensors have been used for indoor occupancy estimation, such as pyro-electric infrared (PIR) sensors [4], ultrasonic sensors [5], and microphones [6]. The author will work on the collected data from surrounding parameters, such as temperature, humidity, air pressure and CO2 levels, and present a performance analysis on the models trained on these parameters. |
| first_indexed | 2024-10-01T02:42:54Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/140536 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:42:54Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1405362023-07-07T18:46:50Z Occupancy modelling using data driven models Lee, Gabriel Hanjie Soh Yeng Chai School of Electrical and Electronic Engineering eycsoh@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Indoor occupancy information is key to office and home automation systems. It is used as an input for the control of indoor lighting systems [1] and Heat, Ventilation and Air-conditioning (HVAC) systems [2]. HVAC technology ensures constant supply of good quality air and thermal comfort for occupants to live and work using designed heating, filtration and ventilation systems. As our society steadily progresses towards a sustainable future by reducing ecological footprints, more emphasis and attention has been given to the issue of building energy optimization. Studies have also shown that around one-third of the energy consumed in buildings can be saved using occupancy-based control [3]. As such, a great amount of attention has been given to energy efficiency issues in designing and improving our buildings today. A conventional way to estimate the occupancy level in a particular room is to employ numerous sensors in order to completely capture the occupancy profile of the entire environment. Data collected from multi-camera videos coupled with pattern recognition technology can accurately estimate the number of indoor occupants, however, these methods require expensive hardware and are not often used due to their intrusive nature which brings privacy concerns. Thus, many non-intrusive and non-terminal-based types of sensors have been used for indoor occupancy estimation, such as pyro-electric infrared (PIR) sensors [4], ultrasonic sensors [5], and microphones [6]. The author will work on the collected data from surrounding parameters, such as temperature, humidity, air pressure and CO2 levels, and present a performance analysis on the models trained on these parameters. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-30T09:22:40Z 2020-05-30T09:22:40Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140536 en A1154-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Lee, Gabriel Hanjie Occupancy modelling using data driven models |
| title | Occupancy modelling using data driven models |
| title_full | Occupancy modelling using data driven models |
| title_fullStr | Occupancy modelling using data driven models |
| title_full_unstemmed | Occupancy modelling using data driven models |
| title_short | Occupancy modelling using data driven models |
| title_sort | occupancy modelling using data driven models |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/140536 |
| work_keys_str_mv | AT leegabrielhanjie occupancymodellingusingdatadrivenmodels |