用生成模型预测德国日内电价路径，提升交易盈利。
Probabilistic intraday electricity price forecasting using generative machine learning
- 基于生成神经网络构建电价路径概率预测
- 在固定成交量交易中实现更高利润收益
- 适合电力交易策略研发者参考
欧洲日内电力交易日益重要，亟需更精准的价格预测与决策支持工具。本文提出一种新型生成式神经网络模型，用于生成德国连续时段日内电力价格的概率路径，并据此构建有效的交易策略。模型在统计评估指标上优于两种先进统计基准方法。为评估经济价值，我们设定真实场景下的固定成交量交易，基于路径预测设计多种卖单策略。结果表明，该生成模型所产价格路径带来的利润高于基准方法。研究凸显生成式机器学习在电价预测中的潜力，并强调经济评估的重要性。
原文摘要 · Abstract (English)
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.
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