Electricity Price Forecasting in the Irish Balancing Market

Abstract

Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing market is the closest to real-time and the most volatile among them. Its price forecasting literature is limited, inconsistent and outdated, with few deep learning attempts and no public dataset. This work applies to the Irish balancing market a variety of price prediction techniques proven successful in the widely studied day-ahead market. We compare statistical, machine learning, and deep learning models using a framework that investigates the impact of different training sizes. The framework defines hyperparameters and calibration settings; the dataset and models are made public to ensure reproducibility and to be used as benchmarks for future works. An extensive numerical study shows that well-performing models in the day-ahead market do not perform well in the balancing one, highlighting that these markets are fundamentally different constructs. The best model is LEAR, a statistical approach based on LASSO, achieving a mean absolute error of 32.82 €/MWh, surpassing more complex and computationally demanding approaches with errors ranging from 33.71 €/MWh to 44.55 €/MWh.

Keywords

• Day-ahead market
• Balance market
• Electricity Price Forecasting
• Machine learning
• Deep learning