IRRADIANCE AND CLOUD COVER ESTIMATION IN TROPICAL HUMID REGIONS USING ALL-SLY IMAGES: AN INTEGRATED APPROACH
spectral segmentation, irradiance estimation, all-sky cameras
Short-term atmospheric variability, characteristic of humid tropical regions, poses a critical challenge to the operation and planning of grid-connected photovoltaic systems. In this context, the use of hemispherical images captured by All-Sky cameras has emerged as a promising alternative to traditional solar irradiance estimation methods, offering high temporal resolution, angular detail, and low operational cost. This study proposes an empirical methodology based exclusively on RGB images, aiming to estimate global horizontal irradiance (GHI) efficiently and accessibly, without relying on auxiliary radiometric sensors. The approach integrates spectral segmentation techniques for cloud cover classification and stratified polynomial modeling according to distinct atmospheric regimes. Image processing includes geometric correction, solar position detection, cosine-weighted hemispherical sampling, luminance linearization, and angular adjustment based on the sun's position. The corrected relative luminance is then used as an explanatory variable in polynomial models conditioned by the identified cloud cover. The proposed methodology was applied to a set of images and atmospheric data collected in a humid tropical environment, aiming to assess its applicability in scenarios characterized by high cloud variability and limited infrastructure. The results indicate that the stratified modeling improves the physical representativeness of radiative regimes and supports the integration of the approach into operational systems for solar forecasting and energy management, reinforcing its applicability in real-world scenarios of atmospheric conditions monitoring.