Construction of Training Sample in a Support Vector Regression Short-Term Load Forecasting Model

Power load forecasting has always been a hotspot. Recently, Artificial intelligence and computational intelligence methods have been widely used in the power load forecasting field. SVR (Support Vector Regression), one of computational intelligence methods, has been paid more and more attention for its ability of solving none-liner problem and its high prediction accuracy. Most predicting methods based on SVR prefer researching how to optimize argument of SVR model. for the aim of downsizing the training sample or improve the accuracy, some literatures proposed to get optimal subset from the whole training set or reduce attributes of each sample by using mathematical models. but the result of attribute auto reduction can't intuitive show the relationship between various attributes. Moreover it is difficult to deal with the relation between many attributions which may lead to retain or abandon the attributes improperly. This paper proposed a method to construct training set by not only analyzing the relation between the load data and attributes such as weather factor, but also analyzing the load data self-similarity. the result of load forecasting experiment adopting our method shows that the accuracy of short-term load forecasting can be improved effectively.