Abstract
Even though occupancy inference is of utmost importance for numerous real-time and real-life applications a widely-accepted approach to predict occupancy does not exist. In this paper, an assessment of widely-recommended approaches and data processing for occupancy is overviewed. Furthermore, the correlation and meta-analysis between various sensor features like motion sensing, temperature, humidity, and energy consumption were tested. Random Forest classifier a widely-applied artificial model for occupancy inference prediction is evaluated in 4 different real-life data sets including various features. The results of both a univariate and multivariate model are examined. Random Forest classifier results during an experimental phase are presented to reveal the best model. The outcomes of the current research indicate that even in similar spaces data analysis and correlation have different results while the multivariate model is more accurate than the bivariate model.
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Acknowledgements
The research leading to these results was partially funded by the European Commission “LC- EEB-07-2020 - Smart Operation of Proactive Residential Buildings” - PRECEPT H2020 project (Grant agreement ID: 958284) https://www.precept-project.eu/, accessed on 8 March 2022; and “LC-SC3-B4E-3-2020 Upgrading smartness of existing buildings through innovations for legacy equipment” - Smart2B H2020 project (Grant agreement ID: 101023666) https://www.smart2b-project.eu/, accessed on 2 March 2022.
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Dimara, A. et al. (2022). Environmental Feature Correlation and Meta-analysis for Occupancy Detection - A Real-Life Assessment. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_21
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