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

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.