arXiv 6 Jan 2025 · Statistics — Applications · publishedEnergy Economics (2025) · 3 citations (OpenAlex)
arXiv:2501.02963 · PDF · DOI · OpenAlex · Extracted main text
Power prices can be forecasted using data-driven models or fundamental models. Data-driven models learn from historical patterns, while fundamental models simulate electricity markets. Traditionally, fundamental models have been too computationally demanding to allow for intrinsic parameter estimation or frequent updates, which are essential for short-term forecasting. In this paper, we propose a novel data-driven fundamental model that combines the strengths of both approaches. We estimate the parameters of a fully fundamental merit order model using historical data, similar to how data-driven models work. This removes the need for fixed technical parameters or expert assumptions, allowing most parameters to be calibrated directly to observations. The model is efficient enough for quick parameter estimation and forecast generation. We apply it to forecast German day-ahead electricity prices and demonstrate that it outperforms both classical fundamental and purely data-driven models. The hybrid model effectively captures price volatility and sequential price clusters, which are becoming increasingly important with the expansion of renewable energy sources. It also provides valuable insights, such as fuel switches, marginal power plant contributions, estimated parameters, dispatched plants, and power generation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | P. Beran, A. Vogler, and C. Weber (2021) Multi-day-ahead electricity price forecasting: A comparison of fundamental, econometric and hybrid models | 0.693 | 7 | 1 | 100% |
| 2 | R. A. de Marcos, A. Bello, and J. Reneses (2019) Electricity price forecasting in the short term hybridising fundamental and econometric modelling | 0.693 | 7 | 1 | 100% |
| 3 | J. R. Birge, A. Hortacsu, and J. M. Pavlin (2017) Inverse optimization for the recovery of market structure from market outcomes: An application to the miso electricity market | 0.693 | 6 | 1 | 100% |
| 4 | R. Chen, I. C. Paschalidis, and M. C. Caramanis (2017) Strategic equilibrium bidding for electricity suppliers in a day-ahead market using inverse optimization | 0.693 | 5 | 1 | 100% |
| 5 | P. Gabrielli, M. Wüthrich, S. Blume, and G. Sansavini (2022) Data-driven modeling for long-term electricity price forecasting | 0.693 | 5 | 1 | 100% |
| 6 | V. Gonzalez, J. Contreras, and D. W. Bunn (2011) Forecasting power prices using a hybrid fundamental-econometric model | 0.693 | 5 | 1 | 100% |
| 7 | A. Bello, D. W. Bunn, J. Reneses, and A. Muñoz (2016) Medium-term probabilistic forecasting of electricity prices: A hybrid approach | 0.644 | 4 | 1 | 100% |
| 8 | Z. Liang and Y. Dvorkin (2023) Data-driven inverse optimization for marginal offer price recovery in electricity markets | 0.644 | 4 | 1 | 100% |
| 9 | C. Ruiz, A. J. Conejo, and D. J. Bertsimas (2013) Revealing rival marginal offer prices via inverse optimization | 0.644 | 4 | 1 | 100% |
| 10 | T. Brown, J. Hörsch, and D. Schlachtberger (2017) Pypsa: Python for power system analysis | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 65 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy | 0.511 | 2 | 2 |