Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)

TL;DR

This paper proposes a novel temporal hierarchy forecasting method (THieF) for day-ahead electricity prices, improving accuracy across multiple time resolutions and models, with consistent results over four years in German and Spanish markets.

Contribution

Introduction of THieF, a new approach that reconciles forecasts at different temporal levels, enhancing accuracy and practical applicability in electricity price prediction.

Findings

Up to 13% accuracy improvement across levels

Consistent results over 4-year period

Effective across various model architectures

Abstract

We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products and 2- to 24-hour blocks can significantly (up to 13%) improve accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German and Spanish power markets and across model architectures, including linear regression, shallow feedforward neural networks, gradient-boosted decision trees, and a state-of-the-art, pretrained transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice.