Investigation of Day-ahead Price Forecasting Models in the Finnish Electricity Market

The electricity market is a rather complex market and the prices depend on several different factors. The price dynamics are bound to get even more volatile, with a stronger integration between European electricity markets and the increasing share of renewable energy sources. Therefore, the development of accurate electricity price forecasting methods has increasing importance in the field. This paper investigates the performance of several deep learning models for the Finnish electricity market. The investigation comprehends different architectures types, data aggregation schemes as well as pre-training method. In this manner, this work does not only presents new forecasting methods but also gives valuable comparison between approaches.