Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model

Wiem Fekih Hassen () and Maher Challouf
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Wiem Fekih Hassen: Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany
Maher Challouf: Distributed Information Systems, University of Passau, Innstraße 41, 94032 Passau, Germany

Energies, 2024, vol. 17, issue 13, 1-19

Abstract: The transition towards sustainable energy systems necessitates effective management of renewable energy sources alongside conventional grid infrastructure. This paper presents a comprehensive approach to optimizing grid management by integrating Photovoltaic (PV), wind, and grid energies to minimize costs and enhance sustainability. A key focus lies in developing an accurate scheduling algorithm utilizing Mixed Integer Programming (MIP), enabling dynamic allocation of energy resources to meet demand while minimizing reliance on cost-intensive grid energy. An ensemble learning technique, specifically a stacking algorithm, is employed to construct a robust forecasting pipeline for PV and wind energy generation. The forecasting model achieves remarkable accuracy with a Root Mean Squared Error (RMSE) of less than 0.1 for short-term (15 min and one day ahead) and long-term (one week and one month ahead) predictions. By combining optimization and forecasting methodologies, this research contributes to advancing grid management systems capable of harnessing renewable energy sources efficiently, thus facilitating cost savings and fostering sustainability in the energy sector.

Keywords: renewable energy; machine learning; long short-term prediction; mix integer programming; stacking model; cost minimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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