Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets

Battle Count: 78

Highly relevant to quantitative trading in electricity markets. The paper directly addresses price forecasting across the full market stack (day-ahead, intraday, balancing), which is essential for bidding strategies, arbitrage, risk management, and algorithmic trading in energy markets. The taxonomy of backbone-head-loss provides a structured framework for designing trading models. The shift toward probabilistic forecasting and microstructure-aware models is directly applicable to trading strategy development. However, the paper is a review rather than proposing a specific trading strategy, and electricity market trading has unique constraints (non-storability, network constraints, regulatory rules) that differ from traditional financial markets.

As a review/survey paper, it does not implement a specific model. The implementation complexity of the models discussed ranges from low (MLP, simple LSTM) to very high (MoE-GNN hybrids, foundation-style models like PriceFM covering 24+ countries). The taxonomy framework itself is straightforward to apply for categorizing existing or new models. The paper provides clear definitions of the backbone-head-loss decomposition that can guide model design decisions.

As a review/survey paper, it does not propose a single reproducible model. It references 67 papers and provides a comprehensive taxonomy table (Table I) summarizing 49 studies. Reproducibility depends on the individual referenced works. Some referenced papers (e.g., [18] Lago et al.) provide open-access benchmarking toolkits. The taxonomy framework itself is clearly defined and can be applied to new studies.