Vartholomaios, Argyrios, Karlos, Stamatis, Kouloumpris, Eleftherios, and Tsoumakas, Grigorios. "Short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles." International Conference on Database and Expert Systems Applications. Springer, Cham, 2021.

Author(s): Argyrios Vartholomaios, Stamatis Karlos, Eleftherios Kouloumpris, and Grigorios Tsoumakas


Appeared In: Proceedings of the 1st International Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply (AI-CARES 2021)

Keywords: Time series forecasting, Renewable energy sources, Signal decomposition, Prophet model, Tree-based ensembles


Abstract: Energy production using renewable sources exhibits inherent uncertainties due to their intermittent nature. Nevertheless, the unified European energy market promotes the increasing penetration of renewable energy sources (RES) by the regional energy system operators. Consequently, RES forecasting can assist in the integration of these volatile energy sources, since it leads to higher reliability and reduced ancillary operational costs for power systems. This paper presents a new dataset for solar and wind energy generation forecast in Greece and introduces a feature engineering pipeline that enriches the dimensional space of the dataset. In addition, we propose a novel method that utilizes the innovative Prophet model, an end-to-end forecasting tool that considers several kinds of nonlinear trends in decomposing the energy time series before a tree-based ensemble provides short-term predictions. The performance of the system is measured through representative evaluation metrics, and by estimating the model’s generalization under an industry-provided scheme of absolute error thresholds. The proposed hybrid model competes with baseline persistence models, tree-based regression ensembles, and the Prophet model, managing to outperform them, presenting both lower error rates and more favorable error distribution.