Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the "residual", predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 hours. Analyzing data from the German and Spanish markets, and considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy can be as high as 16% over a 5-year test period covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine.