Middle and long term load forecasting of power system is affected by various uncertain factors. Using clustering method numerous relative factors can be synthesized for the forecasting model so that the accuracy of the load forecasting would be improved significantly. A clustering neural network consisting of logic operators is quoted in this paper, which can be used in mid-long term load forecasting. Applying logic operators and in the fuzzy theory, the algorithm speed of the clustering network will be increased. Although competitive learning algorithm is used here for the network, it solves the dead unit problem and gives more room to select the initial values of the clustering center in the clustering analysis of the history data. The proposed model considers the influences of both history and future uncertain factors. Compared with the traditional methods, the results show that the new algorithm improves the accuracy of load forecasting considerably.
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