Prediction of Voc and Isc of Monocrystalline PV Modules Using Artificial Neural Networks- A Data-Driven Approach
DOI:
https://doi.org/10.62677/IJETAA.2511140Keywords:
Photovoltaic systems, Artificial neural networks, Regression prediction, Outdoor testing, Voc, IscAbstract
This study presents an artificial neural network (ANN)-based predictive model for determining the electrical behavior of a monocrystalline solar photovoltaic (PV) module under real outdoor operating conditions. Seven days of experimental measurements were collected at fifteen-minute intervals from 8:30 AM to 4:30 PM, consisting of solar irradiance, cell temperature, ambient temperature, and relative humidity as inputs, and open-circuit voltage (VOC) and short-circuit current (Isc) as outputs. Multiple ANN architectures were trained using the Levenberg–Marquardt (trainlm) algorithm with a sweep of 1–20 hidden neurons and two activation functions (tansig and logsig). The optimal ANN configuration, consisting of a 4–7–7–2 architecture with tansig activation in both hidden layers, achieved the best performance with an overall mean-squared error (MSE) of 0.012887 and a correlation coefficient of R = 0.9531. Results demonstrate that the ANN effectively captures the nonlinear relationship between environmental parameters and PV electrical output. The findings confirm the suitability of neural networks as accurate surrogate models for PV system monitoring, performance evaluation and predictive diagnostics.
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Copyright (c) 2025 Engr. Jordan N. Velasco, Edzel Grant Asis, Engr. Alex Monsanto, Dr. Alexis John N. Rubio, Mary Anne H. Trinidad, Dr. Maria Amelia E. Damian (Author)
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