Machine learning methodologies for real-world resilient energy systems

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Machine learning methodologies for real-world resilient energy systems

With the growing global population, energy demand and consumption have increased. Higher fossil fuel consumption has led to increased greenhouse gas emissions and global warming. However, the 4th Industrial Revolution, driven by digital, biological, and physical innovations, has shown an increased f...

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Bibliographic Details
Main Author:	Boodhoo, Khivishta
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2024
Subjects:	Machine learning; Energy systems; Energy efficiency; Wind turbine; Diesel generators; Vanadium redox flow batteries
Online Access:	https://eprints.nottingham.ac.uk/79870/

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