Forecasting electricity prices in the Swedish regulation market using random forest

Abstract

Forecasting electricity prices became a very vital issue after the deregulation of the market because the predicted prices enable the market players to make appropriate decision. Price forecasts can be of great importance to grid operators whose responsibility is to keep the grid balanced. Forecasting electricity prices on the regulation power market is quite distinct from that of the spot market in that regulation prices could either be up or down regulated. Practically, only one of this regulation price would possibly be different from the spot price per time. However, irregular breakdown in the market system, makes the up and down regulation prices to diverge from the spot prices especially during the up regulation. This thesis focuses on forecasting electricity prices in regulation market using a machine learning technique random forest. Forecast was made both for regulation price direction and price difference base on different seasons such as Winter, Spring, Summer and Fall. The result was quite reliable as the forecast obtained captures original price differences although the forecast for spring and fall could not capture the sudden jumps in the prices. It is concluded that if the random forest algorithm is deeply tuned, there is high chances of obtaining a more accurate forecast performance.