Speaker
Description
With the increasing integration of renewable energy and evolving power consumption patterns caused by new consumers like electric vehicles and heat pumps, power flows in the electricity grid have become more fluctuating and weather-dependent, challenging grid stability. Accurate power forecasts are essential for grid operators to ensure reliable grid calculations and planning. We present a novel approach combining Multi-Task Learning with a Graph Neural Network (GNN) to predict vertical power flows at trans-formers linking high and extra-high voltage levels. By leveraging an Embedding Multi-Task Learning strategy, our method captures local variations in power flow characteristics. The embedding captures latent node features, enabling weight sharing across all transformers in the node-invariant GNN while distinguishing individual transformer behaviors. The GNN architecture also accounts for dependencies between transformers, recognizing that power flows in an electricity network are interdependent. The proposed method's effectiveness is validated using two real-world datasets from German Transmission System Operators, covering significant portions of the German transmission grid. Results demonstrate that the Multi-Task Graph Neural Network outperforms both standard Neural Networks and standard GNN in power flow prediction, benefiting from the embedding layer. A sign test confirms that our model significantly reduces test RMSE on both datasets compared to benchmark models.
