Machine Learning on EPEX Order Books: Insights and Forecasts

Forecasting electricity prices is an important task in an energy utility and needed not only for proprietary trading but also for the optimisation of power plant production schedules and other technical issues. A promising approach in power price forecasting is based on a recalculation of the order book using forecasts on market fundamentals like demand or renewable infeed. However, this approach requires extensive statistical analysis of market data. In this paper, we examine if and how this statistical work can be reduced using machine learning. Our paper focuses on two research questions: - How can order books from electricity markets be included in machine learning algorithms? - How can order-book-based spot price forecasts be improved using machine learning? We consider the German/Austrian EPEX spot market for electricity. There is a daily auction for electricity with delivery the next day. All 24 hours of the day are traded as separate products.