Is your forecaster smarter than an energy engineer: a deep dive into electricity price forecasting
This addresses the trustworthiness of forecasts for energy market decision-makers, but is incremental as it focuses on evaluating an existing model.
The paper investigates the reliability of a state-of-the-art electricity price forecasting model under extreme conditions, finding that while generally accurate, it struggles with consistency in such scenarios.
The field of electricity price forecasting has seen significant advances in the last years, including the development of new, more accurate forecast models. These models leverage statistical relationships in previously observed data to predict the future; however, there is a lack of analysis explaining these models, which limits their real world applicability in critical infrastructure. In this paper, using data from the Belgian electricity markets, we explore a state-of-the-art forecasting model to understand if its predictions can be trusted in more general settings than the limited context it is trained in. If the model produces poor predictions in extreme conditions or if its predictions are inconsistent with reality, it cannot be relied upon in real-world where these forecasts are used in downstream decision-making activities. Our results show that, despite being largely accurate enough in general, even state of the art forecasts struggle with remaining consistent with reality.