Forecasting Italian electricity market prices using a Neural Network and a Support Vector Regression

This work explores two different techniques for the prediction of the Italian day-ahead electricity market prices, the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted over a 2-year long period, with hourly data of the prices to be predicted and a large set of variables used as predictors (i.e. historical prices, forecast load, wind and solar power forecasts, expected plenty or shortage of hydroelectric production, net transfer capacity available at the interconnections and the gas prices). A Neural Network (NN) and a Support Vector Regression (SVR) are applied on the different predictors to obtain the final forecasts. Different predictors' combinations are analyzed in order to find the best forecast. We compare the NN and SVR to two less sophisticated post-processing methods, i.e. a linear regression (LR) and the persistency (P).