Towards the Prediction of Electricity Prices at the Intraday Market Using Shallow and Deep-Learning Methods

Abstract

The percentage of renewable energies (RE) within power generation in Germany has increased significantly since 2010 from 16.6% to 42.9% in 2019 which led to a larger variability in the electricity prices. In particular, generation from wind and photovoltaics induces high volatility, is difficult to forecast and challenging to plan. To counter this variability, the continuous intraday market at the EPEX SPOT offers the possibility to trade energy in a short-term perspective, and enables the adjustment of earlier trading errors. In this context, appropriate price forecasts are important to improve the trading decisions on the energy market. Therefore, we present and analyse in this paper a novel approach for the prediction of the energy price for the continuous intraday market at the EPEX SPOT. To model the continuous intraday price, we introduce a semi-continuous framework based on a rolling window approach. For the prediction task we utilise shallow learning techniques and present a LSTM-based deep learning architecture. All approaches are compared against two baseline methods which are simply current intraday prices at different aggregation levels. We show that our novel approaches significantly outperform the considered baseline models. In addition to the general results, we further present an extension in form of a multi-step ahead forecast.