Research on Hybrid ARIMA and Support Vector Machine Model in Short Term Load Forecasting

In power system, due to the complexity of the historical load data and the randomness of a lot of uncertain factors influence, the observed historical data showed linear and nonlinear characteristics. As we all known, the autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting, and the SVM model is the recent research trend successfully used in solving nonlinear regression and time series problem. So in this paper, a hybrid methodology that combines both ARIMA and SVM model is presented to take advantage of the unique strength of ARIMA and SVM models in linear and nonlinear modeling. The linear pattern of the historical load data can be dealt with ARIMA, and the nonlinear part with SVM model. The effectiveness of the model has been tested using Hebei province daily load data with satisfactory results. The experimental results showed that the hybrid model can effectively improve the forecasting accuracy achieved by either of the models used separately