Original scientific paper
https://doi.org/10.13044/j.sdewes.d13.0660
Smart Monitoring of Photovoltaic Cells Using Adaptive Neuro-Fuzzy Inference and Cloud Integration for Maximum Power Point Estimation
Mario Eduardo Carbono dela Rosa
; Universidad Nacional Abierta y a Distancia, Bogota, Colombia
Mario A. Millan-Franco
; Universidad Nacional autonoma de México, Ciudad de Mexico, Mexico
Jesús E. Diosa
; Universidad del Valle, Cali, Colombia
Edgar Mosquera Vargas
; Universidad del Valle, Cali, Colombia
Abstract
The growing integration of photovoltaic systems into modern energy networks demands intelligent monitoring solutions to optimize performance and ensure long-term reliability. This study proposes a predictive framework based on an Adaptive Neuro - Fuzzy Inference System for estimating the power output of photovoltaic cells using electroluminescence images and current - voltage curves. The model extracts three key features from electroluminescence images counts of black, grey, and white pixels which reflect internal defects and degradation patterns. These image derived parameters are correlated with electrical performance indicators obtained from current voltage curves to enhance prediction accuracy. By combining the learning capabilities of neural networks with the interpretability of fuzzy logic, the adaptive neuro - fuzzy inference model provides accurate estimations of the normalized maximum power point while maintaining transparency through interpretable fuzzy rules. The system is implemented on a cloud - based platform, enabling real - time data analysis and offering valuable insights for performance assessment and quality control of photovoltaic cells. Experimental validation on a dataset of six hundred crystalline photovoltaic cells demonstrates high predictive accuracy, achieving a mean absolute error of 0.073 and a mean squared error of 0.0084. This approach enhances photovoltaic module efficiency evaluation and supports the development of more effective monitoring strategies in photovoltaic systems.
Keywords
Photovoltaic cells; Power Output Estimation; Electroluminescence Imaging; Renewable Energy; ANFIS; Cloud-Based Monitoring.
Hrčak ID:
348130
URI
Publication date:
23.7.2026.
Visits: 0 *