RT info:eu-repo/semantics/article
T1 Enhancing solar cell classification using mamdani fuzzy logic over electroluminescence images: A comparative analysis with machine learning methods
A1 Mateo Romero, Héctor Felipe
A1 Carbonó de la Rosa, Mario Eduardo
A1 Hernández Callejo, Luis
A1 González Rebollo, Miguel Ángel
A1 Cardeñoso Payo, Valentín
A1 Alonso Gómez, Víctor
A1 Gallardo Saavedra, Sara
K1 Fuzzy Logic
K1 Photovoltaic
K1 Electroluminescence
K1 Machine Learning
AB This work presents a Mamdani Fuzzy Logic model capable of classifying solar cells according to their energetic performance. The model has 3 different inputs: The proportion of black pixels, gray pixels, and white pixels. One additional output for informing of possible bad inputs is also provided. The three values are obtained from an Electroluminescence image of the cell. The model has been developed using cells whose performance has been obtained by measuring the Intensity-Voltage Curves of the cells. The performance of the model has been shown by testing it with a validation set, obtaining a 99.0% of accuracy, when other methods such as Ensemble Classifiers and Decision Trees obtain a 97.7%. This shows that the presented model is capable of solving the problem better than traditional Machine Learning methods.
SN 1865-0929
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/66000
UL https://uvadoc.uva.es/handle/10324/66000
LA eng
NO Mateo-Romero, H.F. et al. (2024). Enhancing Solar Cell Classification Using Mamdani Fuzzy Logic Over Electroluminescence Images: A Comparative Analysis with Machine Learning Methods. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, vol 1938. Springer, Cham. https://doi.org/10.1007/978-3-031-52517-9_11
DS UVaDOC
RD 19-dic-2024