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

2026-09-19

AI Summary

本文提出一种基于时间层次的预测框架（THieF），通过协调小时电价和日内价差预测，提高电力市场预测精度和经济效益。

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.

Problem

• day-ahead electricity markets
• intraday price spreads
• forecasting
• battery arbitrage
• temporal hierarchy forecasting

Innovation

• Temporal Hierarchy Forecasting
• Electricity Price Spreads
• Intraday Prices
• Forecast Reconciliation
• Arbitrage Profits