The Clustering Neural Network Based on Fuzzy Competitive Learning Algorithm for Middle and Long Term Load Forecasting
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. The new method introduces the neural network into the fuzzy clustering and found the new model of mid-long term load forecasting. The method also makes improvement in the learning algorithm. It adopts the fuzzy competitive learning and solves the binary results of the network output and makes the change rate of the weight matrix speed up. So the convergence speed is improved effectively. 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.
mid-long term load forecasting clustering neural network fuzzy competitive learning algorithm information ezpansion
国际会议
2008 China International Conference on Electricity Distribution (CICED 2008)(2008中国国际供电会议)
广州
英文
1-5
2008-12-10（万方平台首次上网日期，不代表论文的发表时间）