Short Term Load Forecasting Using Clustering based Support Vector Regression

This paper proposes a short term load forecasting(STLF) method by using clustering based support vector regression model.The proposed method is first based on self-organizing feature map(SOFM) that can discover the similar input data and cluster them into several subsets in an unsupervised strategy.Then,several SVR models are constructed in corresponding to the subsets;each SVR model is trained with its corresponding subset.Due to the similarity in training data and the reduction of the amount of training data for each SVR model,the proposed method can forecast with more accurate results while enhancing the training speed.Comparison of simulation results of different methods based on the historical load data proves the feasibility of this model.