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  4. Day-Ahead Electricity Load Prediction Based on Calendar Features and Temporal Convolutional Networks
 
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2023
Conference Paper
Title

Day-Ahead Electricity Load Prediction Based on Calendar Features and Temporal Convolutional Networks

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.
Author(s)
Richter, Lucas
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Bauer, Fabian
Klaiber, Stefan  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Bretschneider, Peter  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Mainwork
Theory and Applications of Time Series Analysis and Forecasting  
Conference
International Conference on Time Series and Forecasting 2021  
DOI
10.1007/978-3-031-14197-3_16
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
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