A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact حسب Samer Gowid (15955152)

"…Nonetheless, the consideration of irradiance, electrical, thermal, and weather parameters (scenario IV) yielded higher identification accuracy. The results showed a decrease in the MSE of V_mp by 74.3% (from 1.6 V to 0.411 V), and in the MSE of I_mp by 95% (from 4.4e−6 A to 2.16e−7 A), respectively. …"