Variability and ramp rates of PV systems are increasingly important to understand and model for grid stability as PV penetration levels rise. Using satellite imagery to identify cloud types and patterns can predict irradiance variability in areas lacking sensors. With satellite imagery covering the entire U.S., this allows for more accurate integration planning and power flow modeling over wide areas. Satellite imagery of southern Nevada was analyzed and methods for image stabilization, cloud

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... tection, and textural classification of clouds were developed and tested. Artificial Neural Networks using imagery as inputs were trained on ground-based irradiance measurements and were tested and showed some promise as a means for modeling the irradiance and variability for a location at a one minute resolution without needing many ground based irradiance sensors.