Newest papers

Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets

Arkadiusz Lipiecki, Nikolaos Kourentzes, Rafal Weron · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.23223 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

天前电价预测支持交易和存储决策,但对于电池仲裁,预测日内电价差比比预测个别小时价格更为重要。在这里,我们展示了一套时间等级预测(THieF)框架,该框架将每小时电价预测和所有日内电价差相匹配,从而在两个主要的欧洲电力市场和三个不同的预测架构中不断提高业绩。使用德国和西班牙五年的非样本数据,我们获得了高达19.7%的准确性提高,以及与不一致的小时价格预测相比高达10.4%的利润增加。即使在高度准确的TabPFN预测基础模型中,这些收益仍然存在。我们的结果表明,利用经济相关预测目标之间的一致性关系可以提高预测准确度和决策价值,而且对于更好的统计

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Arbitrage, Electricity, Electricity price forecasting, Econometrics, Electricity market, Economics

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