Abstract:
This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar
cell, focusing on the energy and exergy aspects. A signifcant research gap exists in the comprehensive
integration of numerical models with advanced machine-learning approaches, specifcally emotional
artifcial neural networks (EANN), to simulate and optimize the electrical characteristics and efciency
of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to
simulate the system’s electrical characteristics, including open-circuit voltage, short-circuit current,
system resistances, maximum power point characteristics, and characteristic curves. Mathematical
equations for calculating efciency levels under varying operational conditions were developed.
The system’s operational and electrical parameters, alongside environmental conditions such as
solar radiation, wind speed, and ambient temperature, were empirically observed and documented
over a day. A comparative analysis was conducted to validate the model by comparing its results
with manufacturer data and experimental observations. During the trial from 7:00 to 17:00, energy
efciency varied from 10.34 to 14.00%, averaging 13.6%, while exergy efciency ranged from 13.57
to 16.41%, with an average of 15.70%. The results from the EANN model indicate that the proposed
method for forecasting energy, exergy, and power is feasible, ofering a signifcant reduction in
computational expense compared to traditional numerical models. The integration of numerical
modeling with EANN enhances simulation accuracy and the developed equations enable real-time
efciency calculations. Empirical validation under varying environmental conditions improves
predictive capabilities for solar panel performance. Additionally, operational efciency assessments
aid in better design and deployment of solar energy systems, and computational costs for large-scale
solar energy simulations are reduced.
