  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.
