Abstract
The sustainability of the electricity system is closely related to the analysis of smart electricity meter data, which plays an important role in enhancing energy management and overall grid operation. The widespread use of household smart meters generates a substantial volume of data, offering an opportunity to enhance overall energy management by analyzing household electricity usage data. However, when faced with an influx of new data, traditional clustering methods require re-clustering all the data from scratch, which can be computationally intensive. To address the challenge of handling the ever-increasing data, an incremental clustering algorithm proves to be the most suitable choice. Incremental learning, accomplished through incremental clustering, provides a straightforward and effective approach. In this research, the proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm updates load patterns without relying on overall daily load curve clustering. The CGMIC algorithm first extracts load patterns from new data and then either intergrades the existing load patterns or forms new ones. Real-world electricity smart meter data, such as the IITB Indian Residential Energy Dataset, is utilized to validate the proposed system. The effectiveness of the proposed system is assessed using metrics like the silhouette score and Davis Bouldin index, employing the incremental K-means algorithm. The insight gained from the proposed system contribute directly to sustainable development goals. By effectively identifies changes in residential electricity consumption behavior, providing valuable insights for utility companies to optimize electricity load management.