Smart Grid Big Data Architecture Design based on Improved K-means Algorithm

This study focuses on the analysis and improvement of clustering algorithms for smart grid big data analysis. The goal is to address the unique challenges posed by smart grid data, such as high dimensionality, sparsity, and the presence of categorical features. The proposed architecture incorporates enhancements to the traditional K-means algorithm, including seed selection initialization, specific distance metrics, and convergence criteria. The experimental evaluation involves a real-world smart grid dataset and employs evaluation metrics such as the Silhouette Coefficient, Rand Index, and Davies-Bouldin Index. The results demonstrate the effectiveness and efficiency of the improved algorithms, showcasing well-separated clusters, high similarity to reference clusterings, and compact cluster structures. The findings highlight the importance of appropriate distance metrics, such as cosine similarity, and the utilization of domain knowledge in the initialization process. Comparisons with traditional K-means clustering reveal the superior performance of the improved algorithms in terms of cluster quality. The implications of these findings for smart grid data analysis include improved energy management, anomaly detection, load forecasting, and grid optimization. Further research is warranted to validate the algorithms on diverse datasets and under different scenarios. The proposed architecture and improved K-means algorithms offer valuable insights for researchers and practitioners seeking to analyze and optimize smart grid big data.