Application of SVM based on immune genetic fuzzy clustering algorithm to short-term load forecasting

Support vector machine (SVM) has been applied to load forecasting field widely. However, if the training data has much noise and redundancy, the generalized performance of SVM will be weakened, so this can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting model based on immune genetic fuzzy clustering algorithm (IGA-SVM) is presented, using immune genetic fuzzy clustering algorithm to preprocess historical load data, and then extract training samples from clustered data, and the result is that both processing speed and forecasting accuracy are improved. At last, apply this model to short-term load forecasting, and it shows more generalized performance and better forecasting accuracy compared with the methods of single SVM and BP neural networks.