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inconsistent and outdated, with few deep learning attempts and no public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.

通过比较统计学、机器学习和深度学习模型在爱尔兰平衡市场上的表现，本研究发现短期电力市场和平衡市场是基本不同的构造，并提出了一种基于 LASSO 的 LEARN 模型，其在平衡市场上的预测表现优于更复杂和计算要求更高的方法。

爱尔兰平衡市场的电力价格预测

Electricity Price Forecasting in the Irish Balancing Market

Prosumer operators are dealing with extensive challenges to participate in
short-term electricity markets while taking uncertainties into account.
Challenges such as variation in demand, solar energy, wind power, and
electricity prices as well as faster response time in intraday electricity
markets. Machine learning approaches could resolve these challenges due to
their ability to continuous learning of complex relations and providing a
real-time response. Such approaches are applicable with presence of the high
performance computing and big data. To tackle these challenges, a Markov
decision process is proposed and solved with a reinforcement learning algorithm
with proper observations and actions employing tabular Q-learning. Trained
agent converges to a policy which is similar to the global optimal solution. It
increases the prosumer's profit by 13.39% compared to the well-known stochastic
optimization approach.

研究采用强化学习算法中的马尔可夫决策过程，通过机器学习等技术应对电力市场中的各种挑战，并取得了 13.39% 的增长。