On the importance of the long-term seasonal component in day-ahead electricity price forecasting

- A new class of Seasonal Component AutoRegressive (SCAR) models is introduced.
- Electricity prices are decomposed into a trend-seasonal and a stochastic component.
- Both components are modeled independently, their forecasts are combined.
- Significant accuracy gains can be achieved compared to commonly used approaches.

In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring - contrary to a common belief - an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves.