Pesenti, Antoine;
O’Sullivan, Aidan;
(2025)
Explaining deep neural network models for electricity price forecasting with XAI.
Energy and AI
, 21
, Article 100532. 10.1016/j.egyai.2025.100532.
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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.
| Type: | Article |
|---|---|
| Title: | Explaining deep neural network models for electricity price forecasting with XAI |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.egyai.2025.100532 |
| Publisher version: | https://doi.org/10.1016/j.egyai.2025.100532 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Electricity price forecasting, EPF, Explainable methods, XAI, Explainable AI, SHAP, Gradient, Saliency map |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210515 |
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