An Evolutionary Stacked Ensemble for Improving Accuracy and Computational Efficiency in Daily Photovoltaic Energy Prediction

This study presents a computationally efficient stacked ensemble model for daily photovoltaic energy prediction. The ensemble combines five base regressors: extreme gradient boosting, decision trees, random forests, neural networks, and k-nearest neighbors. A weighted linear combination merges these...

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Detalles Bibliográficos

Principais autores:	Bilal Rinchi, Sameer Al-Dahidi, Mohammed E. B. Abdalla, Osama Ayadi, Mohammad Alrbai
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2026-01-01
Series:	IEEE Access
Assuntos:	Photovoltaic energy forecasting differential evolution optimization stacked ensemble learning loss function sustainable computing
Acceso en liña:	https://ieeexplore.ieee.org/document/11400713/
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