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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 marke
