Sensing by proxy: Occupancy detection based on indoor CO2 concentration. Jin, M., Bekiaris-Liberis, N., Weekly, K., Spanos, C., & Bayen, A. In UBICOMM 2015, volume 14, 2015. (Best Paper Award)![Sensing by proxy: Occupancy detection based on indoor CO2 concentration [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Link abstract bibtex 1 download Sensing by proxy, as described in this study, is a sensing paradigm which infers latent factors by “proxy” measurements based on constitutive models that exploit the spatial and physical features in the system. In this study, we demonstrate the efficiency of sensing by proxy for occupancy detection based on indoor CO2 concentration. We propose a link model that relates the proxy measurements with unknown human emission rates based on a data-driven model which consists of a coupled Partial Differential Equation (PDE) – Ordinary Differential Equation (ODE) system. We report on several experimental results using both a CO2 pump that emulates human breathing, as well as measurements of actual occupancy by performing controlled field experiments, in order to validate our model. Parameters of the model are datadriven, which exhibit long-term stability and robustness across all the occupants experiments. The inference of the number of occupants in the room based on CO2 measurements at the air return and air supply vents by sensing by proxy outperforms a range of machine learning algorithms, and achieves an overall mean squared error of 0.6569 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and occupancy-aware services to achieve occupancy comfort and energy efficiency. The significance of this study is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. The proposed framework can be also applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses.
@INPROCEEDINGS{2015_3C_sbp,
title={Sensing by proxy: Occupancy detection based on indoor CO2 concentration},
author={Jin, Ming and Bekiaris-Liberis, Nikolaos and Weekly, Kevin and Spanos, Costas and Bayen, Alexandre},
booktitle={UBICOMM 2015},
url_pdf={sbp.pdf},
url_link={https://www.iaria.org/conferences2015/UBICOMM15.html},
abstract={Sensing by proxy, as described in this study, is a sensing paradigm which infers latent factors by “proxy” measurements based on constitutive models that exploit the spatial and physical features in the system. In this study, we demonstrate the efficiency of sensing by proxy for occupancy detection based on indoor CO2 concentration. We propose a link model that relates the proxy measurements with unknown human emission rates based on a data-driven model which consists of a coupled Partial Differential Equation (PDE) – Ordinary Differential Equation (ODE) system. We report on several experimental results using both a CO2 pump that emulates human breathing, as well as measurements of actual occupancy by performing controlled field experiments, in order to validate our model. Parameters of the model are datadriven, which exhibit long-term stability and robustness across all the occupants experiments. The inference of the number of occupants in the room based on CO2 measurements at the air return and air supply vents by sensing by proxy outperforms a range of machine learning algorithms, and achieves an overall mean squared error of 0.6569 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and occupancy-aware services to achieve occupancy comfort and energy efficiency. The significance of this study is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. The proposed framework can be also applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses.},
keywords={Smart city, Energy system, Data mining},
volume={14},
year={2015},
note={<font style="color:#FF0000">(Best Paper Award)</font>}
}
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