Synthetic Data Generation for Wind Energy Forecasting

Abstract

This paper examines the effectiveness of various synthetic data generation methods for deterministic wind power forecasting. Specifically, this work evaluates four approaches—Gaussian Mixture Models (GMMs), t-Copula, DoppelGANger, and FCPFlow—by comparing the forecasting performance, measured using Mean Absolute Error and Root Mean Squared Error, of models trained on synthetic versus real datasets. Our findings indicate that statistical methods (such as GMM and t-Copula) achieve notably better performance under limited data availability. However, the deep generative model FCPFlow yields superior results when sufficient training data is available. These findings suggest that the choice of synthetic data generation method should be informed by the specific data availability context.