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

Richter, LucasLucasRichterBauer, FabianFabianBauerKlaiber, StefanStefanKlaiberBretschneider, PeterPeterBretschneider2023-04-122023-04-122023https://publica.fraunhofer.de/handle/publica/43965610.1007/978-3-031-14197-3_16Transmission 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.enDay-Ahead Electricity Load Prediction Based on Calendar Features and Temporal Convolutional Networksconference paper