2448. Bridging Data-Driven and Fundamental Models: Estimating a Merit Order for Probabilistic Day-Ahead Electricity Price Forecasting
Invited abstract in session WA-44: Forecasting methods and electricity markets, stream Energy Economics & Management.
Wednesday, 8:30-10:00Room: Newlyn 1.01
Authors (first author is the speaker)
| 1. | Paul Ghelasi
|
| House of Energy Markets and Finance, University of Duisburg-Essen | |
| 2. | Florian Ziel
|
| House of Energy Markets and Finance, University of Duisburg-Essen |
Abstract
Power prices can be forecasted using data-driven or fundamental models. Data-driven models learn from historical patterns, while fundamental models simulate electricity markets. Traditionally, fundamental models have been too computationally demanding for intrinsic parameter estimation or frequent updates, which are essential for short-term forecasting. We propose a novel data-driven fundamental model that combines both approaches. By estimating the parameters of a fully fundamental merit order model from historical data, similar to data-driven models, we eliminate the need for fixed technical parameters or expert assumptions. This allows direct calibration to observations. The model is efficient enough for rapid parameter estimation and forecast generation. Applied to German day-ahead electricity prices, it outperforms both classical fundamental and purely data-driven models. It captures price volatility and sequential price clusters, which are increasingly important with growing renewables. Additionally, the framework enables probabilistic forecasting by modeling entire distributions of input data. By fitting probability distributions to all input variables and sampling from them, the model generates a range of possible price outcomes, effectively capturing forecast uncertainty.
Keywords
- Forecasting
- Electricity Markets
- Economic Modeling
Status: accepted
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