窃取利润：日前电力市场的基于价差的时序层次预测
Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets
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- Wrocław University of Science and Technology（弗罗茨瓦夫理工大学）
- Indicio Technologies AB（因迪西奥科技公司）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究提出时序层次预测框架，通过协调小时电价与日内价差预测，在欧洲两市场提升精度达19.7%、利润达10.4%，并揭示统计精度提升未必带来经济决策改善。
中文摘要 AI 辅助
日前电价预测支持交易和储能决策，但对于电池套利而言，预测日内价差比预测单个小时电价更为相关。在此，我们展示了一种时序层次预测（THieF）框架，该框架联合协调小时电价与所有日内价差的预测，在欧洲两个主要电力市场和三种不同的预测架构中持续提升了性能。利用来自德国和西班牙的五年样本外数据，相对于未协调的小时电价预测，我们获得了高达19.7%的精度提升和高达10.4%的利润增益。这些增益即使对于高度准确的预训练TabPFN基础模型也依然存在。我们的结果表明，利用经济相关预测目标之间的连贯关系可以同时提高预测准确性和决策价值，并且更好的统计预测并不一定意味着更好的经济决策。
英文摘要
Day-ahead electricity price forecasts support trading and storage decisions, but for battery arbitrage predicting intraday price spreads is more relevant than predicting individual hourly prices. Here we show that a temporal hierarchy forecasting (THieF) framework that jointly reconciles forecasts of hourly electricity prices and all intraday price spreads consistently improves performance across two major European electricity markets and three different forecasting architectures. Using five years of out-of-sample data from Germany and Spain, we obtain accuracy improvements of up to 19.7% and profit gains of up to 10.4% relative to unreconciled hourly price forecasts. The gains persist even for a highly accurate pretrained TabPFN foundation model. Our results demonstrate that exploiting coherent relationships between economically relevant forecasting targets can improve both predictive accuracy and decision value, and that better statistical forecasts do not necessarily imply better economic decisions.
