Mira Watermeyer, Thomas Möbius, Oliver Grothe, Felix Müsgens
arXiv 18 Apr 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2304.09336 · PDF · DOI · OpenAlex · Extracted main text
The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.
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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 | Thomas Möbius, Mira Watermeyer, Oliver Grothe, and Felix Muesgens (2023) Enhancing Energy System Models Using Better Load Forecasts self | 1.000 | 5 | 3 | 100% |
| 2 | Jesus Lago, Grzegorz Marcjasz, Bart De Schutter, and Rafał Weron (2021) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 1.000 | 5 | 3 | 100% |
| 3 | Florian Ziel and Rafał Weron (2017) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks | 0.928 | 4 | 3 | 100% |
| 4 | Jakub Nowotarski and Rafał Weron (2017) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.843 | 3 | 3 | 100% |
| 5 | Tao Hong, Pierre Pinson, Yi Wang, Rafał Weron, Dazhi Yang, and Hamid… (2020) Energy Forecasting: A Review and Outlook | 0.737 | 3 | 2 | 100% |
| 6 | Jakub Nowotarski and Rafał Weron (2015) Computing electricity spot price prediction intervals using quantile regression and forecast averaging | 0.737 | 3 | 2 | 100% |
| 7 | Andreas Schröder, Friedrich Kunz, Jan Meiss, Roman Mendelevitch, and… (1861) Current and Prospective Costs of Electricity Generation until 2050 | 0.693 | 9 | 1 | 100% |
| 8 | ENTSO-E Transparency Platform (2021) Actual Generation per Production Type, 2021c | 0.693 | 6 | 1 | 100% |
| 9 | Open Power System Data (2020) Data Package National Generation Capacity. Version 2019-12-02., 2020b | 0.644 | 4 | 1 | 100% |
| 10 | Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Trocc… (2016) Probabilistic Global Energy Forecasting Competition 2014 and beyond | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 110 scored citations.