רמי פוזיס

Cloud cover is a key factor influencing photovoltaic energy production; however, monitoring remains limited by the sparse deployment of ground-based instruments and by intermittent satellite coverage. This study presents a machine learning framework, called Cloud Cover Estimation from Solar Panel data (CCESP). We use standard inverter logs from existing photovoltaic systems as distributed weather sensors and recover a meteorological variable that inverters do not measure directly. Unlike simulation-based explorations or specially instrumented prototypes, we provide the first large-scale, multi-site demonstration on real operational photovoltaic data without requiring additional hardware. We evaluate CCESP on three commercial photovoltaic sites in the Middle East with nine years of inverter records, paired with cloud cover data from multiple weather providers. The ensemble-of-inverters approach, implemented with XGBoost and contextual features, yields a mean absolute error of 10.96% ((Formula presented) cloud cover) and a Spearman correlation of 0.75 across sites. This error is lower than the smallest disagreement between any two of the independent weather providers (mean absolute error (Formula presented) ) and approaches the accuracy of ground-based optical instruments reported in prior studies. In a sliding-window evaluation, the error remains stable across seven test years. At an independent site in the United States, the Spearman correlation is 0.74, compared with 0.73 at the Middle East sites under the same training target. Widely deployed photovoltaic infrastructure can serve as a cost-free supplementary sensing network that complements satellite and ground-based systems, particularly in regions with limited meteorological coverage, and operators can use the estimated cloud cover in solar power forecasting and grid integration.