Explaining deep neural network models for electricity price forecasting with XAI
This work addresses the need for better understanding of complex electricity markets for stakeholders, though it is incremental in combining existing XAI techniques with DNNs.
The paper tackled the problem of forecasting electricity prices using deep neural networks and applied explainable AI methods to understand market dynamics, achieving improved interpretability across five electricity markets with the introduction of novel SSHAP concepts.
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.