2023/10/05 by Julius Trebbien, Sebastian Pütz, Trebbien, Julius +9
Decision Sciences · Engineering · #Artificial intelligence #Computer science #Data Analysis #Econometrics #Economics #Electricity #Electricity market #Electricity price #Electricity price forecasting #Energy Load and Power Forecasting #Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph) #Probabilistic forecasting #Probabilistic logic #Statistics and Probability (physics.data-an) #Stock Market Forecasting Methods #Volatility (finance)
paper · pdf · doi:10.48550/arxiv.2310.03339
openalex publication_date 2023/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.