Researchers have developed two novel methodologies for forecasting energy market curves, specifically for the EPEX SPOT Day-Ahead market. The first approach utilizes a low-dimensional parametric representation with eXtreme Gradient Boosting for deterministic point forecasts. The second, and more significant contribution, employs conditional Denoising Diffusion Probabilistic Models to generate distributions of plausible energy market curves, capturing distributional variability. Evaluations using French EPEX data from 2021 to 2024 demonstrated that the diffusion-based model achieved higher profits and smaller gaps to an oracle benchmark in a price-maker storage optimization problem compared to the parametric method. AI
IMPACT These generative models could improve energy market trading strategies and risk management by providing more nuanced forecasts.
RANK_REASON The cluster contains an academic paper detailing new methodologies for forecasting energy market curves using machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
- conditional Denoising Diffusion Probabilistic Models
- EPEX SPOT Day-Ahead market
- Julian Gutierrez
- eXtreme Gradient Boosting
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