A Competitive Scenario Forecaster using XGBoost and Gaussian Copula

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.