  In this paper we propose a new method for probabilistic forecasting of
electricity prices. It is based on averaging point forecasts from different
models combined with expectile regression. We show that deriving the predicted
distribution in terms of expectiles, might be in some cases advantageous to the
commonly used quantiles. We apply the proposed method to the day-ahead
electricity prices from the German market and compare its accuracy with the
Quantile Regression Averaging method and quantile -- as well as expectile-based
historical simulation. The obtained results indicate that using the expectile
regression improves the accuracy of the probabilistic forecasts of electricity
prices, but a variance stabilizing transformation should be applied prior to
modelling.
