Forecasting electricity prices in Finland's regulating market using neural networks
Nthakomwa, Kettie Susan (2018)
Nthakomwa, Kettie Susan
Julkaisun pysyvä osoite on
Electricity price forecast is vital to market bidders for strategic bidding and to grid operators so as to keep power balanced in the grid. However, studies have shown that electricity spot prices are not that easy to forecast since they are highly volatile. Due to deregulation of electricity markets, markets became more efficient and prices more competitive but this has resulted into regulation markets to be more volatile. This study focuses on forecasting balancing electricity prices which is the difference between regulation and spot prices, and also predicting regulation direction in the Finnish regulation market. Neural networks, which have the ability to handle complex relations in the data are used. A feedforward neural network is implemented in Matlab, where Levenberg-Marquardt Backpropagation algorithm, which works at minimizing the error, is employed in training the network model. The networks are trained, validated and tested for high accuracy and then used to make predictions for balancing price and balancing direction. The results obtained show that the fitted model for balancing price has a fit performance of 44:01% and the balancing direction fitted model has 45:9% performance.