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

Jiaxun Guo, Manar Amayri, Wentao Fan, Nizar Bouguila

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.