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| Home > Publications database > Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs |
| Home > Publications database > Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs |
| Contribution to a conference proceedings/Contribution to a book | FZJ-2024-03054 |
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2023
IEEE
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Please use a persistent id in citations: doi:10.1109/ISGTEUROPE56780.2023.10407112 doi:10.34734/FZJ-2024-03054
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.
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