Energy

Abstract The aim of this thesis was to predict the wind power production volume of a large geographical region given the Numerical Weather Prediction data (NWP) over the region using deep learning. Accurate production volume predictions is important for power grid balancing, production planning, and price estimation. Having an accurate forecast for the upcoming wind power production volume has become more and more important in the past years due to the fast increasing number of installed wind turbines and installed total production capacity.

Abstract With the growing inclusion of renewable energy sources, developing price models for intraday trading has become an essential task for many market participants in order to optimize the decision-making process. Yet the available literature on the topic has not been keeping up with the pace of increased intraday trading activity. We predict prices in the final hour prior to delivery on the German intraday market, utilizing Deep Learning techniques. This thesis looks into the usage of feed-forward neural networks and recurrent neural networks (LSTM and GRU).