Dr. Alfredo Sánchez García works with physical and mathematical modeling of photovoltaic (PV) systems across the value chain, grounded in solar cell physics. He applies machine learning methods for PV performance analysis and manufacturing diagnostics, and develops analytical and computationally efficient models for I–V characteristics, maximum power point behavior, and PV performance under non-ideal conditions such as shading and temperature effects.
Education
PhD in Solar Cell Physics (2018–2022)
University of Agder (UiA)
Thesis: Theoretical Studies in Solar Cell Physics
Trial Lecture: Application of Machine Learning to Material Science. Focus on analytical modeling of solar cell performance, maximum power point behavior
Competence and research areas
Solar cell physics and device modeling.
Machine learning for PV performance and manufacturing analysis.
Analytical modeling of temperature coefficients, maximum power point (MPP), and I–V characteristics.
PV array behavior under shading, bifacial operation, and Nordic conditions.
Silicon ingot and wafer characterization (lifetime, resistivity, EL, LPS).
Melt and ingot modeling (CGSim for Czochralski process, OpenFOAM for impurity transport).
Linkedin
https://www.linkedin.com/in/alfredo-sanchez-garcia/
ResearchGate
https://www.researchgate.net/profile/Alfredo-Sanchez-Garcia
ORCID
https://orcid.org/0000-0001-6219-8348
