Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
Establishes a reproducible benchmark framework for electricity price forecasting (EPF) using publicly available data, addressing the field's lack of standardized comparison datasets Compares six deep learning architectures (state-space, MLP, RNN, and Transformer-based) across multiple market settings with emphasis on cross-market generalization Simulates low-data target-market conditions through zero-shot, one-shot, and few-shot learning paradigms on the Germany-Luxembourg (DE-LU) bidding zone i
Analysis
TL;DR
- Establishes a reproducible benchmark framework for electricity price forecasting (EPF) using publicly available data, addressing the field's lack of standardized comparison datasets
- Compares six deep learning architectures (state-space, MLP, RNN, and Transformer-based) across multiple market settings with emphasis on cross-market generalization
- Simulates low-data target-market conditions through zero-shot, one-shot, and few-shot learning paradigms on the Germany-Luxembourg (DE-LU) bidding zone in 2024
- N-HiTS and NBEATSx emerge as top performers in limited-data scenarios, while Transformer-based models achieve comparable accuracy but demand more adaptation and tuning
- Careful feature selection and hyperparameter tuning significantly impact model performance, with marginal differences observed between the strongest architectures
Why It Matters
This work addresses a critical gap in the EPF research community by providing a standardized, reproducible evaluation framework that enables fair comparison across diverse deep learning approaches. For AI practitioners working in energy markets or time-series forecasting, it offers practical insights into model selection under data-scarce conditions—a common real-world constraint. The cross-market generalization focus is particularly relevant as energy markets increasingly interconnect across borders.
Technical Details
- Models evaluated: Six deep learning architectures spanning state-space models, MLPs, RNNs, and Transformer-based models (including N-HiTS and NBEATSx), tested on day-ahead electricity price forecasting
- Learning regimes: Zero-shot, one-shot, and few-shot learning paradigms used to simulate low-data target-market conditions, reflecting practical deployment scenarios where historical data is limited
- Dataset: Standardized dataset for the Germany-Luxembourg (DE-LU) bidding zone in 2024, incorporating calendar features, historical price data, and market-derived features
- Evaluation focus: Cross-market generalization capability, with emphasis on reproducible benchmarking using publicly available electricity market data
- Key finding: Performance differences between top-performing models are often small, suggesting that feature engineering and hyperparameter tuning may be as important as architecture choice
Industry Insight
- Energy market operators and grid planners should prioritize N-HiTS and NBEATSx for deployment in new or data-scarce markets, as they demonstrate superior few-shot generalization without extensive retraining
- The marginal performance gaps between top models suggest that organizations should invest more in feature engineering, data quality, and hyperparameter optimization than in chasing novel architectures
- As European electricity markets continue to interconnect, cross-border forecasting frameworks like this one will become essential for traders and grid operators needing reliable price predictions across multiple bidding zones with limited local data history
Disclaimer: The above content is generated by AI and is for reference only.