Vol. 2 No. 11 (2025)

We are pleased to present Issue 11 of the IJETAA. This issue presents the work of Velasco et al. on predicting the electrical performance of monocrystalline solar modules using artificial neural networks. Their optimized ANN architecture (4–7–7–2) successfully forecasts open-circuit voltage (Voc) and short-circuit current (Isc) under real outdoor conditions, achieving a correlation coefficient of R = 0.9531 based on environmental parameters including solar irradiance, temperature, and humidity. This data-driven approach offers valuable applications for PV system monitoring, performance evaluation, and predictive diagnostics in renewable energy installations.