arXiv 1 Jun 2024 · Statistics — Applications · publishedRenewable and Sustainable Energy Reviews (2025) · 20 citations (OpenAlex)
arXiv:2406.00326 · PDF · DOI · OpenAlex · Extracted main text
The recent energy crisis starting in 2021 led to record-high gas, coal, carbon and power prices, with electricity reaching up to 40 times the pre-crisis average. This had dramatic consequences for operational and risk management prompting the need for robust econometric models for mid to long-term electricity price forecasting. After a comprehensive literature analysis, we identify key challenges and address them with novel approaches: 1) Fundamental information is incorporated by constraining coefficients with bounds derived from fundamental models offering interpretability; 2) Short-term regressors such as load and renewables can be used in long-term forecasts by incorporating their seasonal expectations to stabilize the model; 3) Unit root behavior of power prices, induced by fuel prices, can be managed by estimating same-day relationships and projecting them forward. We develop interpretable models for a range of forecasting horizons from one day to one year ahead, providing guidelines on robust modeling frameworks and key explanatory variables for each horizon. Our study, focused on Europe's largest energy market, Germany, analyzes hourly electricity prices using regularized regression methods and generalized additive models.
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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 | R. Steinert and F. Ziel (2019) Short-to mid-term day-ahead electricity price forecasting using futures | 0.811 | 4 | 2 | 100% |
| 2 | F. Ziel and R. Steinert (2018) Probabilistic mid-and long-term electricity price forecasting | 0.693 | 7 | 1 | 100% |
| 3 | P. Gabrielli, M. Wüthrich, S. Blume, and G. Sansavini (2022) Data-driven modeling for long-term electricity price forecasting | 0.693 | 5 | 1 | 100% |
| 4 | R. A. de Marcos, A. Bello, and J. Reneses (2019) Electricity price forecasting in the short term hybridising fundamental and econometric modelling | 0.693 | 5 | 1 | 100% |
| 5 | A. Wagner, E. Ramentol, F. Schirra, and H. Michaeli (2022) Short-and long-term forecasting of electricity prices using embedding of calendar information in neural networks | 0.644 | 4 | 1 | 100% |
| 6 | P. Beran, C. Pape, and C. Weber (2019) Modelling german electricity wholesale spot prices with a parsimonious fundamental model–validation & application | 0.644 | 2 | 2 | 100% |
| 7 | A. G. Billé, A. Gianfreda, F. Del Grosso, and F. Ravazzolo (2023) Forecasting electricity prices with expert, linear, and nonlinear models | 0.644 | 2 | 2 | 100% |
| 8 | S. Chai, Q. Li, M. Z. Abedin, and B. M. Lucey (2023) Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives | 0.644 | 2 | 2 | 100% |
| 9 | S. Emiliozzi, F. Ferriani, and A. G. Gazzani (2023) The european energy crisis and the consequences for the global natural gas market | 0.644 | 2 | 2 | 100% |
| 10 | C. Fezzi and L. Mosetti (2020) Size matters: Estimation sample length and electricity price forecasting accuracy | 0.644 | 2 | 2 | 100% |
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