Probabilistic Forecasting of Day-Ahead Electricity Prices and their
  Volatility with LSTMs
Julius Trebbien Sebastian Pütz Benjamin Schäfer Heidi S. Nygård Leonardo Rydin Gorjão Dirk Witthaut
Accurate forecasts of electricity prices are crucial for the management of
electric power systems and the development of smart applications. European
electricity prices have risen substantially and became highly volatile after
the Russian invasion of Ukraine, challenging established forecasting methods.
Here, we present a Long Short-Term Memory (LSTM) model for the
German-Luxembourg day-ahead electricity prices addressing these challenges. The
recurrent structure of the LSTM allows the model to adapt to trends, while the
joint prediction of both mean and standard deviation enables a probabilistic
prediction. Using a physics-inspired approach - superstatistics - to derive an
explanation for the statistics of prices, we show that the LSTM model
faithfully reproduces both prices and their volatility.