Forecasting the Regulating Price in the Finnish Energy Market using the Multi-Horizon Quantile Recurrent Neural Network
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 markeEnergy is something we use on a daily basis. Among other things we use it to charge our phones and turn on the lights. But what determines the price of energy? In the Nordics, energy is traded like a commodity by the market. That means the price is set according to supply and demand. And while the demand is easy to forecast, determining what will be produced is harder. Forecasting production accur