Research on Classification of Power Load Data Based on LIBSVM

In order to effectively classify the increasingly massive load data, the method of applying support vector machine to load classification is studied. By comparing and selecting the C-SVC classification and the RBF kernel function, the optimization method of the grid search is used to find the optimal parameter combination of the LIBSVM classifier and establish the model. The visualization accuracy and index function are used to observe the classification accuracy of each class. Operations such as weighting the unbalanced samples are conducted to adjust the accuracy gap between large and small samples. Experimental results show that the method is feasible in load classification.