Refining Nonparametric Mixture Models with Explainability for Smart Building Applications

Nonparametric mixture models are a powerful and flexible approach to data clustering; they account for uncertainty by using Bayesian inference to obtain a posterior distribution over the model's parameters and a better fit to the data. Additionally, nonparametric mixture models can adaptively adjust the number of components to fit the data. Incorporating asymmetric generalized Gaussian distribution (AGGD) within the mixture framework extends the capabilities of the widely used Gaussian mixture model (GMM) by adding parameters that control the shape and the skewness of the per-component distribution, which enables the model to capture diverse and complex data patterns. Furthermore, we incorporate explainability within our proposed infinite asymmetric generalized Gaussian mixture model (IAGGMM) to provide interpretable insights into the clustering results, enhancing the model's practicality and transparency. This integration facilitates a deeper understanding of the underlying data structures and the rationale behind the model's decisions, fostering trust and promoting the adoption of our approach in various real-world scenarios. In this study, we explore the application of occupancy estimation for optimizing energy efficiency and facility management in smart buildings. Our approach demonstrates superior performance in modelling complex and asymmetric data distributions, resulting in improved accuracy and adaptability for occupancy level estimation. Therefore, we achieve the optimal trade-off between model complexity and accuracy.