Explaining deep neural network models for electricity price forecasting with XAI

Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models. • Five major electricity markets are explained with different XAI methods applied to their DNN models for electricity price forecasting. • New SSHAP explainable method is introduced to deal with EPF high-dimensional DNN models. • XAI explanations reveal insights into the feature transformations inside the black-box models.