Key Words:
battery testing, state-of-health, state-of-charge, electrochemical impedance spectroscopy (EIS), neural network model
We propose to integrate advanced machine learning algorithms and remote sensing technologies
into digital twins of hydropower plants and other infrastructures. This innovative approach aims to
predict and manage water quality and quantity and optimize power generation, thereby increasing
operational resilience and efficiency. It will also employ predictive analytics within the digital twin
to forecast future trends in water quality and quantity based on historical data, real-time
observations, and climate models.
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