Predictions of Electricity Prices in Different Time Periods With Lasso

Sammanfattning

When the big data time comes, people also need to keep pace with the times to seek and develop tools that can deal with the vast amount of information. In this project, lassois applied to build parametric models of electricity prices based on different affecting factors. Thereafter, the models are used to predict the electricity prices 8 days forward for three different time periods. We compare their prediction performances in terms of normalized mean square error (NMSE) and identify dominant factors of the electricity prices in different time periods using lasso. The results show that a model that spans over a 24 hourlong period gives the lowest NMSE, followed by one spanning over a two hour long period where the electricity prices are leading up to a peak value. The model that obtains the highestNMSE is from a two hour long period, where the electricity prices have a peak value. Besides, we also analyze potential reasons for the results.