Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor

Mohamed Khalifa Boutahir (), Abdelaaziz Hessane, Yousef Farhaoui, Mourade Azrour, Mbadiwe S. Benyeogor and Nisreen Innab ()
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Mohamed Khalifa Boutahir: STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Abdelaaziz Hessane: STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Yousef Farhaoui: STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Mourade Azrour: STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Mbadiwe S. Benyeogor: Institute of Physics, University of Munster, 48149 Munster, Germany
Nisreen Innab: Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh 13713, Saudi Arabia

Sustainability, 2024, vol. 16, issue 13, 1-11

Abstract: Solar radiation prediction plays a crucial role in renewable energy management, impacting various decision-making processes aimed at optimizing the utilization of solar resources and promoting sustainability. Ensemble regression methods, notably VotingRegressor, have emerged as promising tools for accurate solar radiation forecasting. By integrating predictions from multiple base estimators, ensemble methods have the potential to capture intricate patterns inherent in solar radiation data. However, achieving optimal predictive performance with ensemble methods heavily relies on the careful weighting assigned to each base estimator, presenting a significant challenge. In this study, a novel approach is presented to enhance solar radiation prediction by utilizing meta-learning techniques to optimize the weighting mechanism in the VotingRegressor ensemble. Meta-learning, a subfield of machine learning focusing on learning algorithms across different tasks, provides a systematic framework for learning to learn. This enables models to adapt and generalize more effectively to new datasets and tasks. Our proposed methodology demonstrated significant improvements, with the VotingRegressor with meta-learning techniques achieving an RMSE of 8.7343, an MAE of 5.42145, and an R² of 0.991913. These results mitigate the need for manual weight tuning and improve the adaptability of the VotingRegressor to varying solar radiation conditions, ultimately contributing to the sustainability of renewable energy systems. The methodology involves a comprehensive exploration of meta-learning techniques, encompassing gradient-based optimization, reinforcement learning, and Bayesian optimization.

Keywords: ensemble regression; meta-learning techniques; sustainable energy forecasting; solar radiation prediction; VotingRegressor (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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