Deep Learning Models Compared for Cross-Border Electricity Price Forecasting
Key takeaways
- Standardized benchmarks are crucial for comparing electricity price forecasting models effectively.
- N-HiTS and NBEATSx models show strong performance in low-data electricity market forecasting.
- Transformer models can be accurate but demand more adaptation and tuning for optimal results.
- Feature selection and hyperparameter tuning significantly influence forecasting model accuracy.
Who benefits
Summary
This study evaluates six deep learning models for electricity price forecasting across multiple market settings, focusing on generalization and performance in low-data scenarios. It establishes a reproducible framework for consistent model evaluation using public electricity market data.
Why it matters
Professionals in energy trading, grid management, and financial analysis need accurate electricity price forecasts to optimize operations, manage risk, and make informed investment decisions. This research provides insights into reliable deep learning models for these critical predictions, especially in complex cross-border markets.
How to implement this in your domain
- 1Evaluate N-HiTS and NBEATSx models for electricity price forecasting in data-limited or new market contexts.
- 2Develop a standardized data pipeline for electricity market data, including calendar, historical price, and market-derived features.
- 3Implement a reproducible framework for comparing forecasting models, ensuring consistent metrics and datasets.
- 4Investigate the impact of careful feature selection and hyperparameter tuning on model performance in your specific market.
- 5Consider zero-shot, one-shot, or few-shot learning techniques when expanding forecasting to new regions with minimal historical data.
Original post by Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer
"arXiv:2608.17091v1 Announce Type: new Abstract: While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many studies have relied on different d…"
View on XOriginally posted by Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer on X · view source
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