Local Learning-ARIMA adaptive hybrid architecture for hourly electricity price forecasting

The paper proposes a hybrid architecture for electricity price forecasting. The proposed architecture combines the advantages of the easy-to-use and relatively easy-to-tune Autoregressive Integrated Moving Average (ARIMA) models and the approximation power of local learning techniques. The architecture is robust and more accurate than the individual forecasting methodologies on which it is based, since it combines a reliable built-in linear model (ARIMA) with an adaptive dynamic corrector (Lazy Learning algorithm). The corrector model is sequentially updated, in order to adapt the whole architecture to varying market conditions. Detailed simulation studies show the effectiveness of the proposed hybrid learning methods for forecasting the volatile Hourly Ontario Energy Prices (HOEPs) of the Ontario, Canada, electricity market.