Analysing drivers and interdependencies in European electricity markets using XAI
Quick Answer
This study combines deep neural networks with explainable AI techniques to analyze electricity price determinants across 39 European bidding zones, revealing that renewable sources, especially solar, significantly influence prices despite their lower generation share, while gas prices remain a key driver.
Key Points
- Utilizes SHAP and SSHAP for feature contribution analysis in electricity pricing.
- Identifies solar energy as a key price influencer despite its low generation share.
- Gas prices consistently drive electricity market dynamics across Europe.
- Highlights interconnections as crucial for understanding price dynamics.
- Constructs a synthetic EU-wide market to explore integrated pricing scenarios.
Paper Resources
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~2 min readAbstract:Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpretability limits their usefulness for understanding the underlying drivers of price formation. This paper addresses this gap by combining DNN models with explainable artificial intelligence (XAI) techniques to analyse the determinants of electricity prices across 39 European bidding zones. We employ SHAP (SHapley Additive exPlanations) to quantify feature contributions and apply and extend SSHAP, an aggregation framework to improve interpretability in high-dimensional settings. The analysis identifies that renewable energy sources, particularly solar, play a disproportionately important role in price formation despite their lower share in total power generation. Gas prices remain a dominant and consistent driver across electricity markets, while interconnections significantly shape price dynamics, highlighting the strong interdependence of European electricity systems. In addition, a synthetic EU-wide electricity market is constructed to explore the counterfactual scenario of a fully integrated market with a single price.
| Comments: | 12 pages |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Economics (econ.GN) |
| Cite as: | arXiv:2606.19118 [cs.AI] |
| (or arXiv:2606.19118v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19118 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Antoine Pesenti [view email]
[v1]
Wed, 17 Jun 2026 14:32:39 UTC (5,216 KB)
— Originally published at arxiv.org
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