Electricity price forecasting based on transfer function models for period-decoupled time series

In electricity markets, accurate price forecasting provides crucial information for market participant to make reasonable competing strategies so as to maximize their benefits. This paper presents a price forecasting model based on Transfer Function Models for period-decoupled time series. The effect of the load on the electricity price can be fully taken into account. The ARIMA model is employed to deal with the nonstationary price and load series, and forecasting models for every hour period are developed independently. The numerical example based on the California market data shows that the proposed model could improve the accuracy of forecasting.