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Analysing drivers and interdependencies in European electricity markets using XAI

XAIを用いた欧州電力市場における要因と相互依存性の分析 (AI 翻訳)

Antonio Pesenti, A. O'Sullivan

2026-06-17#エネルギー転換Origin: EU対象セクター: power
原典: https://www.semanticscholar.org/paper/a11d570f0739db622b61b13a3dcdd331a2b7629e

🤖 gxceed AI 要約

日本語

本論文は、ディープニューラルネットワークと説明可能AI（XAI）を用いて、欧州39の入札地域における電力価格の決定要因を分析した。SHAPを用いて特徴量の寄与を定量化し、再生可能エネルギー（特に太陽光）が価格形成に不均衡に重要な役割を果たすこと、ガス価格が依然として支配的であること、系統連系が価格に強い影響を与えることを示した。さらに、完全統合されたEU単一価格市場の反実仮想シナリオを構築した。

English

This paper combines deep neural networks with explainable AI (XAI) to analyze electricity price drivers across 39 European bidding zones. Using SHAP, it finds that renewable energy (especially solar) plays a disproportionately important role despite lower generation share, gas prices remain a dominant driver, and interconnections significantly shape prices. A counterfactual EU-wide integrated market is constructed.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

欧州の電力市場分析は、日本の電力自由化や再エネ導入拡大における市場設計への示唆を与える。特にXAIを用いた価格要因分析は、日本のJPEXやエリア間連系線の運用においても応用可能である。

In the global GX context

This paper provides insights into the complex drivers of European electricity prices, highlighting the role of renewables and interconnections. The XAI methodology offers a transparent approach to understanding market dynamics, relevant for global energy transition policies and market integration efforts.

👥 読者別の含意

🔬研究者:Offers a methodology combining DNNs and SHAP for analyzing electricity market drivers, with a case study across 39 European zones.

🏢実務担当者:Can inform energy trading and risk management teams about key price drivers and the impact of renewables and interconnections.

🏛政策担当者:Provides evidence on the importance of solar and gas in price formation, useful for market design and integration policies.

📄 Abstract（原文）

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.

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