  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inconsistent and outdated, with few deep learning attempts and no public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, which outperforms
more complex and computationally demanding approaches.
