An ensemble approach for enhanced Day-Ahead price forecasting in electricity markets

Electricity price forecasting (EPF) is a crucial aspect of daily trading operations, enabling market participants to make informed decisions regarding their bidding strategies. This paper explores a day-ahead price forecasting system that harnesses the potential of multiple machine learning (ML) models and their synergistic integration. This approach is designed to capitalize on the strengths of these models while also accounting for the unique characteristics of energy markets. For this purpose, several aggregation models were developed combining the predictions from ML models based on historical evaluations of their performance. The main objective of this approach is to enhance prediction accuracy by shifting the focus away from rigid model selection and instead prioritizing a data-centric approach, by focusing on data quality rather than rigid model selection. As a case study, the German energy market was examined due to its pivotal role within the EU system. The experimental results from this study provide valuable insights into the proposed system's effectiveness and functionality.