RT info:eu-repo/semantics/article
T1 Enhancing photovoltaic cell classification through Mamdani Fuzzy logic: a comparative study with machine learning approaches employing electroluminescence images
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
A1 Morales Aragones, José Ignacio
K1 Fuzzy logic
K1 Photovoltaic
K1 Electroluminescence
K1 Machine learning
K1 3322 Tecnología energética
AB This study introduces a Mamdani Fuzzy Logic model designed to classify solar cells based on their energetic performance. The model incorporates three distinct inputs, namely the proportions of black pixels, gray pixels, and white pixels, extracted from Electroluminescence images of the cells. Additionally, an output is included to signal potential issues with input data. The development of the model involved utilizing cells with known performance, determined through the measurement of Intensity-Voltage Curves. The efficacy of the model was demonstrated through testing with a validation set, yielding an accuracy rate of 99.0% in the Polycrystalline dataset and 98% in the Monocrystalline. In comparison, traditional machine learning methods such as Ensemble Classifiers and Decision Trees achieved inferior accuracy rates. These results show the superior problem-solving capability of the presented Fuzzy Logic model over conventional machine-learning approaches.
PB Springer
SN 2192-6352
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/76925
UL https://uvadoc.uva.es/handle/10324/76925
LA eng
NO Progress in Artificial Intelligence, 2025, vol. 14, p. 49-59.
NO Producción Científica
DS UVaDOC
RD 25-ago-2025