A Competitive Scenario Forecaster using XGBoost and Gaussian Copula
Name
Denys Kolomiiets
Abstract
In recent years scenario forecasting has been explored and developed by multiple authors. It is a useful technique for setting such as renewable energy production, which is extremely important for a society transitioning from fossil fuel energy generation. Currently, one of the methods to approach the task of scenario forecasting are generative models. The primary goal of this thesis is to develop an approach that outperforms the current best model, using the decision tree model method. This work also discusses possible improvements for decision tree models in scenario forecasting setting. Our approach has surpassed the performance of generative models, making it a solid new baseline for future researchers to beat.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Novin Shahroudi, Meelis Kull
Defence year
2023