Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs

TL;DR

This paper introduces an LSTM-based probabilistic forecasting model for day-ahead electricity prices in Germany-Luxembourg, effectively capturing price trends and volatility amid recent market challenges, using a physics-inspired superstatistics approach.

Contribution

The paper presents a novel LSTM model that jointly predicts prices and volatility, incorporating superstatistics to explain price dynamics in a volatile electricity market.

Findings

LSTM model accurately reproduces price levels and volatility.

Probabilistic forecasts outperform traditional methods.

Superstatistics provides a theoretical basis for observed price behaviors.

Abstract

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.