Probabilistic Forecasting of Imbalance Prices in the Belgian Context
This work addresses a domain-specific challenge for energy market participants in Belgium, representing an incremental improvement over existing methods.
The paper tackles the problem of forecasting imbalance prices in the Belgian energy market by proposing a novel two-step probabilistic approach, which outperforms deterministic models, multi-layer perceptrons, and Gaussian Processes in accuracy.
Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel two-step probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists of computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and a widely used probabilistic technique, Gaussian Processes.