An advanced ensemble clustering approach for data partitioning and mining to optimize performance in variable refrigerant flow systems

Abstract

This study presents a data mining-based approach to develop clustering ensemble models for data partitioning of variable refrigerant flow (VRF) system, with the aim of improving the quality of pattern detection and laying a solid foundation for further data mining. This approach mainly includes three parts. First, data preprocessing combines handling outliers, feature filtering, stable data filtering and data normalization. Secondly, consensus clustering is used to obtain an appropriate number of clusters. To generate base clusterings, we applied three clustering algorithms - k-means, self-organizing maps, and fuzzy C-means - in a 1:1:1 ratio. These base clusterings were selected based on the adjusted rand index as the underlying selection criterion. For clustering ensemble, we utilized three integration methods, namely K-modes, latent class analysis, and linkage clustering ensemble. Finally, analyze the metric difference and time consumption of the integration clustering results of different methods, and obtain a suitable data partitioning scheme for VRF system. This case study examines the utilization of the proposed method to analyze the operation data of a VRF system in an office building. The results indicate that the quality of the clustering derived by the ensemble clustering model is notably superior to that of the individual base clustering. Moreover, the latent class analysis method demonstrates remarkable advantages in achieving top-quality clustering outcomes. These findings suggest that the proposed method can be effective in optimizing VRF system operation while leveling up the accuracy of data analysis. Ensemble models have considerable merit in devising fault identification and diagnosis techniques, optimizing operation, and exploring the links between VRF systems and smart grids.