arXiv:2601.20226v2 Announce Type: replace Abstract: We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day\char45 Ahead market, emphasizing generative models as a way to recover distributional variability. The first is a low\char45 dimensional parametric representation that yields deterministic point forecasts; the second is a high\char45 dimensional order\char45 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, a
The increasing volatility and complexity of energy markets, driven by renewables integration and geopolitical factors, necessitate more sophisticated forecasting tools.
Accurate and granular energy market curve forecasting is crucial for grid stability, investment decisions in energy infrastructure, and managing operational costs for industrial consumers.
The ability to generate high-dimensional forecasts of entire energy market curves, rather than just point forecasts, will allow for better risk management and strategic bidding.
- · Energy traders
- · Grid operators
- · Renewable energy producers
- · Large industrial energy consumers
- · Energy market participants reliant on cruder forecasting methods
- · Less agile power generators
Improved energy market efficiency and reduced price volatility for consumers.
Enhanced investment in and integration of intermittent renewable energy sources due to better predictability.
Potential for new financial products and services based on dynamic energy curve risk assessment and trading.
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