Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/218CF94A-58F9-441B-ADCC-987BAE430BE5 | ||
| Year | 2018 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | Μ. Jin, Ν. Bekiaris-Liberis, Κ. Weekly, C.J. Spanos and A.M. Bayen, "Occupancy detection via environmental sensing," IEEE Trans. Autom. Sci. Eng., vol. 15, no. 2, pp. 443-455, Apr. 2018. doi: 10.1109/TASE.2016.2619720 https://doi.org/10.1109/TASE.2016.2619720 | ||
| Appears in Collections |
Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on 'proxy' measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation-ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants.Note to Practitioners-Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and geofencing to achieve occupancy comfort and energy efficiency. The significance of this paper 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. Parameters of the model are data-driven, which exhibit long-term stability and robustness across all the occupants' experiments. The proposed framework can also be applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses. The data set and algorithm code are available to assist the comparison study.
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