Day-ahead Electricity Price Forecasting Using Time Series Model Based on Nonparametric Generalized Auto Regressive Conditional Heteroskedasticity

In electricity market the price sequence fluctuates frequently,periodically and stochastically,consequently price spikes often appear and it impacts the accuracy of price forecasting.Based on wavelet transform and nonparametric generalized auto regressive conditional heteroscedasticity(NPGARCH) model,a time sequence model for the forecasting of day-ahead price was proposed.Utilizing wavelet transform,the historical price sequence was decomposed and reconstructed into a general picture sequence and a detail sequence,and corresponding auto-regressive integrated moving average(ARIMA) models were built respectively for price forecasting.The NPGARCH model was used for the modeling of stochastic volatility of residual error in forecasting results of ARIMA models to improve both forecasting ability of price spikes and forecasting accuracy of ARIMA models.The proposed models were applied to day-ahead price forecasting of the electricity market of Pennsylvania-New Jersey-Maryland(PJM).Forecasting results of the calculation example show that the proposed model can better fit the characteristics of price sequence that fluctuates intensively,and the forecasting results of day-ahead price are more accurate.