A kernel-based regression model plus scenario generation from forecast errors and a new Support Vector Sorting step produces ensemble price trajectories that improve both statistical accuracy and trading profits over benchmarks on German intraday continuous market data.
arXiv preprint arXiv:2506.00044
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An online regularized multivariate distributional regression method is introduced for high-dimensional probabilistic electricity price forecasting, with a case study on German day-ahead data and an open-source implementation.
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.
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Scenario generation of intraday electricity price paths for optimal trading in continuous markets
A kernel-based regression model plus scenario generation from forecast errors and a new Support Vector Sorting step produces ensemble price trajectories that improve both statistical accuracy and trading profits over benchmarks on German intraday continuous market data.
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Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting
An online regularized multivariate distributional regression method is introduced for high-dimensional probabilistic electricity price forecasting, with a case study on German day-ahead data and an open-source implementation.
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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.