<![CDATA[Partial shading is one of the dominant causes of energy performance degradation in static photovoltaic (PV) systems, especially in tropical environments where dust accumulation and vegetation growth frequently occur. Conventional shading analysis in photovoltaic energy studies is commonly conducted using geometric simulation or irradiance‑based modelling without explicitly identifying physical shading objects on the PV surface. This paper proposes a computer vision‑based approach to quantitatively detect shading objects and statistically evaluate their impact on PV energy performance. A Python–OpenCV framework was developed to calculate pixel‑based shading area on a 10 Wp static PV module. Electrical parameters including voltage, current, and output power were experimentally measured under shading levels ranging from 10% to 100%. Statistical analyses consisting of Pearson correlation, linear regression, significance testing, and one‑way Analysis of Variance (ANOVA) were applied. The results show a very strong negative correlation between shading area and both current and power output (r = −0.98, p < 0.001). Linear regression indicates that shading area explains 96.1% of current variation (R² = 0.961), while ANOVA confirms statistically significant differences in energy output across shading levels (p < 0.01). These findings demonstrate that computer vision‑based shading quantification provides a statistically robust and energy‑oriented framework for analysing performance degradation in static photovoltaic systems.]]>
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