Short-Term Load Forecasting Based on Fuzzy C-Mean Clustering and Weighted Support Vector Machines

In this paper, a new short-term load forecasting method is presented by conjunctive use of fuzzy c- mean clustering algorithm and weighted support vector machines (WSVMs). According to the similarity degree of input samples, the training samples are clustered, i.e., by means of the clustering of study samples the data possessed of homogenous characteristic are chosen and used as the input of forecast model, thus the consistency of data is intensified. It is obvious that newer data are more important for forecasting than older ones. So, according to time, we endow each data a weight factor sh and construct a new learning machines, named WSVMs for regression. Practical application results show that the proposed method can be used as an attractive and effective means for short-term load forecasting.