Forecasting the Regulating Price in the Finnish Energy Market using the Multi-Horizon Quantile Recurrent Neural Network

Abstract: In recent years there has been a large increase in available data from the electric grid in Finland. The availability of both operational as well as financial data enables exploration of forecasting energy prices using deep learning techniques. As a result this thesis implements the Multi-Horizon Quantile Recurrent Neural Network (MQRNN) to forecast the regulating price in the Finnish energy market. The forecast is a rolling window three to eight hours into the future and contains several quantiles. The results suggest that while the central location of the distribution does not change much from the spot price the tails can be long, especially the right tail. Since the model is able to capture changes in the distribution there is indication that the market contains some structure. Finally, after dicussing the results and drawing conclusions some suggestions for future improvements are presented.