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This paper presents a new method-combined use of FCM clustering and support vector regression (SVR) for short term load forecasting in power systems. Using the above advantages of SVR, the complicated nonlinear relationships between some forecasting influence factors and the forecasting load can be regressed. Meanwhile, this paper chooses training samples by fuzzy clustering according to similarity degree of the input samples in consideration of the periodic characteristic of load change. The results of the practical applications of the proposed method show the usefulness of this method, both the precision and speed of load forecasting can be improved.