Power Load Pattern Recognition based on Improved K-Means and SVM Classifier

In order to deeply mine the behavior characteristics of power demand-side users and strengthen the accurate interaction between source, grid and load, it is of great significance to identify and analyze load patterns based on massive user load data. In order to solve the problem that the traditional k-means algorithm is sensitive to the initial clustering center and it is difficult to quantify the number of clusters, this paper uses the improved K-means clustering algorithm to cluster the massive user load data. In the data preprocessing stage, t-SNE dimension reduction technology is introduced, and then the GSA-elbow judgment is used to determine the number of clusters. The Huffman tree is constructed based on the density characteristics and dissimilarity attributes of the data to obtain the initial clustering center and obtain the stable clustering result. Based on load clustering, this paper uses SVM classifier for load pattern recognition to extract user load features, and realizes the pattern recognition of unknown user load data.