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A scaled dirichlet-based predictive model for occupancy estimation in smart buildings

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Abstract

In this study, we introduce a predictive model leveraging the scaled Dirichlet mixture model (SDMM). This data-driven approach offers enhanced accuracy in predictions, especially with a limited training dataset, surpassing traditional point estimation methods. Recent research has highlighted the flexibility of the Dirichlet distribution in modelling multivariate proportional data. Our research extends this by employing a scaled Dirichlet distribution, which incorporates additional parameters, to construct our predictive model. Furthermore, we address the challenge of data imbalance through a novel approach centred on data spread rate, effectively balancing the dataset to optimize model performance. Empirical evaluations demonstrate the model’s efficacy with both synthetic and real datasets, particularly in estimating occupancy in smart buildings.

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Acknowledgements

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC), a start-up grant awarded to Manar Amayri from Concordia University, the National Natural Science Foundation of China (62276106), the Guangdong Provincial Key Laboratory IRADS (2022B1212010006, R0400001-22) and the UIC Start-up Research Fund (UICR0700056-23).

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Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, H3G1T7, QC, Canada

    Jiaxun Guo, Manar Amayri & Nizar Bouguila

  2. Guangdong Provincial Key Laboratory IRADS and Department of Computer Science, Beijing Normal University-Hong Kong Baptist University (BNU-HKBU) United International College, Zhuhai, 519087, Guangdong, China

    Wentao Fan

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  1. Jiaxun Guo
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Guo, J., Amayri, M., Fan, W. et al. A scaled dirichlet-based predictive model for occupancy estimation in smart buildings. Appl Intell 54, 6981–6996 (2024). https://doi.org/10.1007/s10489-024-05543-6

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