Power System Short-Term Load Forecasting Based On Wavelet Transform And Support Vector Machine

At first, this paper discusses the constituents and characteristics of the electric load, analyzes and compares the merits and shortcomings of some forecasting methods. Through the research the load serials can be considered as a linear combination of sub-serials characterized by different frequencies. Therefore the load serials are decomposed into different sub-serials by using the proper wavelet function and the resolution level. Each of them varies with specific periodicities and regularization. Different SVM models are designed to capture each sub-serial's characteristics. After all sub-serials are forecasted, we consider the influence of weather especially temperature. So the whole predicted load series would be composed or reconstructed. Using the model this paper puts forward, we apply the method to a region to compare and analyze. The outcome indicates that the method has strong adaptability and well accuracy in load forecast and has remarkable superiority than other models.