Comparison of different models for forecasting of Czech electricity market
|Author:||Mgr. Vladimír Kunc|
|Year:||2017 - summer|
|Leaders:|| prof. PhDr. Ladislav Krištoufek Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||There is a demand for decision support tools that can model the electricity
markets and allows to forecast the hourly electricity price. Many different approach
such as artificial neural network or support vector regression are used
in the literature. This thesis provides comparison of several different estimators
under one settings using available data from Czech electricity market.
The resulting comparison of over 5000 different estimators led to a selection of
several best performing models. The role of historical weather data (temperature,
dew point and humidity) is also assesed within the comparison and it
was found that while the inclusion of weather data might lead to overfitting,
it is beneficial under the right circumstances. The best performing approach
was the Lasso regression estimated using modified Lars.