A market-rule-informed neural network for imbalance electricity price forecasting matches generic deep learning accuracy while using substantially fewer parameters and less training time.
Interpretable transformer model for capturing regime switching effects of real-time electricity prices
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A literature review of imbalance price forecasting methods across European markets that notes the move toward data-driven models and advocates for standardized benchmarks and downstream-value metrics.
A structured review organizes deep learning models for electricity price forecasting via a backbone-head-loss taxonomy and identifies gaps in intraday and balancing market applications.
citing papers explorer
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A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
A market-rule-informed neural network for imbalance electricity price forecasting matches generic deep learning accuracy while using substantially fewer parameters and less training time.
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A review of imbalance price forecasting algorithms in Europe: algorithms, metrics and the way forward
A literature review of imbalance price forecasting methods across European markets that notes the move toward data-driven models and advocates for standardized benchmarks and downstream-value metrics.
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Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets
A structured review organizes deep learning models for electricity price forecasting via a backbone-head-loss taxonomy and identifies gaps in intraday and balancing market applications.