Abstract
Transmission system operator (TSO) have to ensure grid stability economically. This requires highly accurate load forecasts for the transmission grids. The ENTSO-E transparency platform (ETP) currently provides a load estimation and a day-ahead load prediction for different TSO in Germany. This paper shows a hybrid model architecture of a feedforward network based on calendar features to extract the general behaviour of a time-series and a temporal convolutional network to extract the relations between short-historical and future time-series values. This research shows a significant improvement of the current day-ahead load forecast and additionally a model robustness while training with a non-optimal data set.
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Acknowledgements
The work was financially supported by BMWi in Germany (Bundesministeriums für Bildung und Forschung) under the project “Bauhaus.MobilityLab”.
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Richter, L., Bauer, F., Klaiber, S., Bretschneider, P. (2023). Day-Ahead Electricity Load Prediction Based on Calendar Features and Temporal Convolutional Networks. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis and Forecasting. ITISE 2021. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-14197-3_16
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DOI: https://doi.org/10.1007/978-3-031-14197-3_16
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