Contents I. Introduction
Power load forecasting is the most important part of load forecasting in power system. With an accurate load forecast, several controls such as economic dispatch, energy transactions, security analysis, unit commitment and generator maintenance scheduling are all benefited. For a long time, most of the load-forecasting theory and methods are based on time series analysis, statistical models which include linear regression models, stochastic process, autoregressive and moving averages (ARMA) models and Box-Jenkins methods. With the quick development of artificial intelligence, some forecasting methods which own outstanding learn ability gain global application in short-term load forecasting, such as artificial neural networks (ANN)[1], fuzzy logic approaches[2] and hybrid architccturc[3] based on self organizing (SOM), etc. Though there are a lot of forecasting models, no single one has performed well enough because each model can take just several or usually only one relevant factor into consideration. The combination forecasting method, which can fully utilizes the useful information from several models, becomes one of the most popular subjects in the field of forecasting methods [4]–[6]. Various combination forecasting methods have been proposed in the last few decades, which can be largely divided into two categories: linear combination forecasting and non-linear combination forecasting.
