The Power System Short-term Load Forecasting Model Based On HHT

Power system short-term load forecast is the basis of power system optimization running. It can affect safety, reliability and economy of power system operation. Thus, to find effective method has great importance for enhancing the prediction precision. Researchers have proposed many effective methods, but there is a lot of room for improvement. This paper use HHT to decompose the load datafirstly, then combine some popular methods,such as neural network,Support Vector Maehine,PSO to forecast the power system short-term load. The main work included:Pretreatment on the load data, applying wright criteria to remove outliers and wavelet algorithm to denoise; use a methed to improve its inherent flaw modal-mixing. A series of IMFs with frequency from high to low can be obtained after decompose the load data, reconstruct the low-frequency signals, then according to each IMF's frequency feature we can chose appropriate model to forecast it. At last we can get the final forecast by add the predicted results of each IMF component. Due to IMF1 has large fluctuation, take temperature and weekday into consideration, otherwise neural network and PSO are used to optimize the combination weights. Simulation results indicate that this method has higher accuracy.Finally, the actual data of power system load in a certain place in Sichuan Province in 2006 is used as the sample to set prediction model,test results and verify the accuracy of the model.