AI model cuts electricity price forecasting errors by 13% using novel hierarchy approach
Researchers have developed a technique that makes electricity price predictions significantly more accurate by reconciling forecasts across different time blocks simultaneously. The method works with any AI model and costs almost nothing extra to run, making it immediately practical for power traders and grid operators managing billions in daily transactions.
Originaltitel: Stealing accuracy: Predicting day-ahead electricity prices with temporal hierarchy forecasting (THieF)
<p>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.</p>