Parametric and Generative Forecasts of EPEX Day-Ahead Energy Market Curves

Abstract:We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day-Ahead market, emphasizing generative models as a way to recover distributional variability. The first is a low-dimensional parametric representation that yields deterministic point forecasts; the second is a high-dimensional order-level representation that samples from a conditional distribution of plausible curves. Both model the full curve structure, enabling the analysis of price sensitivity, volume sensitivity, and price impact. The parametric representation uses plateau levels, elastic-region boundaries, and polynomial coefficients, forecast with eXtreme Gradient Boosting. The main contribution is the generative representation, which uses price arrivals and volume-increment marks and is implemented with conditional Denoising Diffusion Probabilistic Models. Using French EPEX data from 2021 to 2024, we evaluate both approaches through curve reconstruction and a price-maker storage optimization problem. The parametric implementation provides a deterministic reference, while the diffusion-based implementation produces distributions of plausible curves and achieves higher realized profits and smaller gaps to an oracle benchmark in the storage application.

Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2601.20226 [cs.LG]

(or arXiv:2601.20226v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.20226 Focus to learn more arXiv-issued DOI via DataCite

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