RT info:eu-repo/semantics/article
T1 ANFIS-based output power estimation in photovoltaic cells using electroluminescence image features
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 Martínez Sacristán, Óscar
A1 Gallardo Saavedra, Sara
A1 Opsino Castro, Adalberto José
K1 Fuzzy logic
K1 Photovoltaic
K1 Electroluminescence
K1 Machine learning
K1 3306 Ingeniería y Tecnología Eléctricas
AB This manuscript introduces two Adaptive Neuro-Fuzzy Inference Systems developed to predict the energy output of Photovoltaic cells. These models are trained using Electroluminescence imagery of the cells for input data along their Current–Voltage curves, which offer insights output power of cells. The input characteristics of the cells are quantified based on pixel distribution and classified into three distinct categories: Black, White, and Gray values. The second model enhances this representation by incorporating an additional fuzzy categorization input, derived from a Mamdani Classifier Fuzzy Logic Model. By combining the rule-based interpretability of Fuzzy Logic with the adaptive learning capabilities of Artificial Neural Networks, the Adaptive Neuro-Fuzzy Inference System (ANFIS) emerges as an alternative to Convolutional Neural Networks (CNNs). This approach contributes to Explainable Artificial Intelligence by addressing one of the major limitations of CNNs—the lack of symbolic knowledge representation, while maintaining robust learning performance. Comparative analysis with other Machine Learning techniques demonstrates the enhanced performance provided by ANFIS models, achieving a Mean Absolute Error (MAE) of 0.053 and a Mean Squared Error (MSE) of 0.007.
PB Springer
SN 1432-7643
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/83754
UL https://uvadoc.uva.es/handle/10324/83754
LA eng
NO Soft Computing, 2026, [online first 21-03-2026]
NO Producción Científica
DS UVaDOC
RD 28-mar-2026