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001025665 1001_ $$0P:(DE-Juel1)192442$$aTrebbien, Julius$$b0
001025665 1112_ $$a2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)$$cGrenoble$$d2023-10-23 - 2023-10-26$$wFrance
001025665 245__ $$aProbabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs
001025665 260__ $$bIEEE$$c2023
001025665 29510 $$a2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) : [Proceedings] - IEEE, 2023. - ISBN 979-8-3503-9678-2 - doi:10.1109/ISGTEUROPE56780.2023.10407112
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001025665 520__ $$aAccurate 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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001025665 7001_ $$0P:(DE-Juel1)190862$$aPütz, Sebastian$$b1
001025665 7001_ $$0P:(DE-HGF)0$$aSchäfer, Benjamin$$b2
001025665 7001_ $$0P:(DE-HGF)0$$aNygård, Heidi S.$$b3
001025665 7001_ $$0P:(DE-HGF)0$$aGorjão, Leonardo Rydin$$b4
001025665 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b5$$eCorresponding author$$ufzj
001025665 773__ $$a10.1109/ISGTEUROPE56780.2023.10407112
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