Probabilistic intraday electricity price forecasting using generative machine learning
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Abstract (arXiv)
The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 28 May 2025.
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15 of 61 references.
References · 15
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3 more
- 2022Aggregating distribution forecasts from deep ensemblescited by 5
- 2020Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processingcited by 4
- 2025Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecastingcited by 2
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Citing papers per year: 2025: 2.
2 papers.