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用于日内电力交易的生成式价格路径预测

文章 arXiv papers · 作者: Jieyu Chen et al.
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总结

本文提出一种生成式神经网络，用于预测德国连续交易市场的日内电价概率分布。该方法不只给出点估计，而是生成可能的价格路径，并在固定交易量的设定下将其用于指导市场卖单的挂单。

作者报告称，该模型在统计指标上与两个统计预测基准相比具有竞争力。在经济评估中，基于其生成路径的策略取得的利润增幅高于基准策略。摘要未说明模型架构、具体下单规则、评估期间、交易成本处理方式或收益增幅。结果仅适用于德国日内市场和所述情景，因此不能证明该方法可推广到其他市场或交易条件。

核心观点

  • 该预测模型生成日内电价的概率路径。
  • 在固定交易量的情景下，生成的路径用于指导卖单。
  • 统计表现与两种基准预测方法进行比较。
  • 报告的交易收益更有利于生成式模型，但摘要缺少评估成本和推广能力所需的细节。

标签

机器学习大宗商品统计学交易执行
全文
# Probabilistic intraday electricity price forecasting using generative machine learning


# Probabilistic intraday electricity price forecasting using generative machine learning









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。