A hybrid ARIMA-ANN model and its Learning Algorithm on Short-term Load Forecasting
【摘要】：正 Because of the complexity of the historical load data and the randomness of a lot of uncertain factors influence, the observed data include the linear and nonlinear parts. The choice of the forecasting model becomes the important influence factor how to improve load forecasting accuracy. ARIMA and ANN are very practical forecasting technology in the short-term electric load forecasting fields. ANN is extensively applied in electric load forecasting especially in recent years. Both ARIMA and ANN have different characteristics. ARIMA is suitable for linear prediction and ANN is suitable for nonlinear prediction. A combined model of ARIMA-ANN is proposed in the text. The linear part of the historical load data can be dealt with ARIMA, and ANN model can deal with the nonlinear part of historical load data. Experimental results indicate that a hybrid ARIMA-ANN model can improve the load forecasting accuracy.