APPLICATION OF RBFN MODEL FOR LOAD FORECASTING BASED ON RANKING MEANS CLUSTERING

A novel clustering method 鈥?Ranking Means Cluster is proposed in this paper. This method is able to avoid local optimal solution with traditional AT-means cluster algorithm and it can also decide the number of clusters to be classified into. With our algorithm, the central vectors of hidden layers in RBF models can be computed and the nodes number and RBF infrastructure can also be decided. Moreover, a new interactive learning scheme is proposed in this paper to choose network parameters. The learning samples are categorized into training samples and testing samples, which lead to stable network structure by adjusting the power values and radius. Comparison of the proposed algorithm with traditional RBFN in power load prediction shows that the former method is more stable and produces more accurate prediction results.