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
Source Excerpt
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) techniq
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