A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

A Genetic Algorithm Approach as a Self-Learning and Optimization Tool for PV Power Simulation and Digital Twinning

A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknow...

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Bibliographic Details
Main Authors: Dorian Esteban Guzman Razo, Björn Müller, Henrik Madsen, Christof Wittwer
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Energies
Subjects:
machine learning
genetic algorithms
auto-calibrated algorithms
photovoltaic systems
parameter estimation
digital simulation
Online Access:https://www.mdpi.com/1996-1073/13/24/6712

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