Effect of Solar Incidence Angle on Photovoltaic Module Power Output: An Experimental Study in Afikpo, Nigeria
J. N. Agwo, C. N. Nwogu, I. Nnanna
Journal of Energy Research and Reviews · pp. 81–93 · Published 16 Sep 2026
10.9734/jenrr/2026/v18i10544Abstract
Aims: This study examined the effect of solar incident angle on the power output of a single 350 W photovoltaic (PV) module in Afikpo, Ebonyi State, Nigeria. Study Design: The study employed an experimental research design to determine the influence of solar incident angle on the power output of a single 350 W PV panel. This approach was adopted because the incident angle was varied while the corresponding electrical parameters were measured under the prevailing environmental conditions. Place and Duration: The study was carried out in Afikpo, Ebonyi State, Nigeria, over eight months, comprising January, February, March, April, May, June, November, and December 2025. Methodology: The measuring instruments used were an arc for adjusting the slope angle, a voltmeter (Alda Model AV 890C digital multimeter) for measuring voltage, and a lux meter for measuring light intensity. A single 350 W PV panel was mounted on an adjustable table to change its orientation, thereby varying the incident angle, while the corresponding effects on current and voltage were measured and recorded. Results: The experimental results showed that optimum power output values varied among the sampled months as the incident angle changed under different environmental conditions. The optimum power outputs reported for the selected months were 32.88 W, 40.77 W, 29.92 W, 29.92 W, and 27.24 W at incident angles of 40°, 0°, 0°, 0°, and 20°, respectively. The experimental results also showed that the PV panel produced its highest and most consistent power outputs when the incident angle was maintained between 0° and 40°, with an average optimum angle of approximately 30°. Conclusion: The study therefore shows that incident angle is an important parameter in PV power performance and provides an experimental basis for developing mathematical and machine-learning models capable of predicting power output and identifying appropriate panel orientations under varying conditions.
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