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A platform for research: civil engineering, architecture and urbanism
Indoor occupancy estimation from carbon dioxide concentration
Highlights This paper developed an indoor occupancy estimator based on CO2 measurements. This paper proposed a feature scaled ELM to identify the estimator. A method to improve the estimator using locally smoothed CO2 data is provided. The proposed estimator was verified in an office room with maximum 28 occupants. x-Tolerance accuracy was introduced to assess the estimator for large occupancy.
Abstract This paper developed an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machine (ELM) but is shown to perform better for the occupancy estimation problem. The measured CO2 concentration suffers from serious spikes. We find that pre-smoothing the CO2 data can greatly improve the estimation accuracy. In real applications, however, we cannot obtain the real-time globally smoothed CO2 data. We provide a way to use the locally smoothed CO2 data instead, which is available in real-time. We introduce a new criterion, i.e. x-tolerance accuracy, to assess the occupancy estimator. The proposed occupancy estimator was tested in an office room with 24 cubicles and 11 open seats. The accuracy is up to 94%percent with a tolerance of four occupants.
Indoor occupancy estimation from carbon dioxide concentration
Highlights This paper developed an indoor occupancy estimator based on CO2 measurements. This paper proposed a feature scaled ELM to identify the estimator. A method to improve the estimator using locally smoothed CO2 data is provided. The proposed estimator was verified in an office room with maximum 28 occupants. x-Tolerance accuracy was introduced to assess the estimator for large occupancy.
Abstract This paper developed an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machine (ELM) but is shown to perform better for the occupancy estimation problem. The measured CO2 concentration suffers from serious spikes. We find that pre-smoothing the CO2 data can greatly improve the estimation accuracy. In real applications, however, we cannot obtain the real-time globally smoothed CO2 data. We provide a way to use the locally smoothed CO2 data instead, which is available in real-time. We introduce a new criterion, i.e. x-tolerance accuracy, to assess the occupancy estimator. The proposed occupancy estimator was tested in an office room with 24 cubicles and 11 open seats. The accuracy is up to 94%percent with a tolerance of four occupants.
Indoor occupancy estimation from carbon dioxide concentration
Jiang, Chaoyang (author) / Masood, Mustafa K. (author) / Soh, Yeng Chai (author) / Li, Hua (author)
Energy and Buildings ; 131 ; 132-141
2016-09-01
10 pages
Article (Journal)
Electronic Resource
English
Indoor occupancy estimation from carbon dioxide concentration
| Online Contents | 2016
Indoor occupancy estimation from carbon dioxide concentration using parameter estimation algorithms
| SAGE Publications | 2022