Improved Kernel Fuzzy C-Means Clustering Method Based on Smart Grid

Abstract

The improved kernel fuzzy clustering method is used to classify the load data of the smart grid accurately. It lays a good foundation for the subsequent work of power load forecasting and provides a more efficient, safe, and reliable direction for the operation of the power system. Firstly, the collected power load data is preprocessed to reduce data redundancy and improve data quality. Secondly, the kernel fuzzy C-means clustering algorithm based on particle swarm optimization is used to cluster the load data with the same power consumption characteristics. Finally, the improved kernel fuzzy clustering method is compared with the fuzzy C-means clustering method through a simulation example to verify the effectiveness of this method.