Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

Abstract This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine l...

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Bibliographic Details
Main Authors: Rasha Elazab, Ahmed Abo Dahab, Maged Abo Adma, Hany Abdo Hassan
Format: Article
Language:English
Published: SpringerOpen 2024-10-01
Series:Energy Informatics
Subjects:
Solar radiation prediction
Supervised machine learning
Neural networks
Energy management system
HOMER
Microgrid
Online Access:https://doi.org/10.1186/s42162-024-00333-3

Internet

https://doi.org/10.1186/s42162-024-00333-3

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