Machine Learning on EPEX Order Books: Insights and Forecasts
This work addresses price forecasting for electricity market participants, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled forecasting German electricity spot market prices by using machine learning models on order book and fundamental data, resulting in neural networks and random forests outperforming traditional statistical models.
This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is developed. Using cross-validation to optimise hyperparameters, neural networks and random forests are proposed and compared to statistical reference models. The machine learning models outperform traditional approaches.