Explaining deep neural network models for electricity price forecasting with XAI

TL;DR

This paper employs deep neural networks for electricity price forecasting and utilizes explainable AI techniques like SHAP and Gradient to interpret the model's decision factors across multiple markets.

Contribution

It introduces novel SSHAP values and SSHAP lines to improve the interpretability of high-dimensional DNN models in electricity market analysis.

Findings

DNN models effectively forecast electricity prices.

Explainable AI methods reveal key market drivers.

Novel SSHAP concepts enhance model interpretability.

Abstract

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.