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Cloud tracking for solar irradiance prediction

We propose a video analytic system to segment and track clouds for the purpose of solar irradiance prediction. Ground-based imaging sensors are used to monitor potential sun occlusions for solar irradiance drop prediction, which can be used to assist power ramp control strategies. Sky images are first rectified to remove fisheye artifacts. Cloud pixels are segmented and classified into low-to-high transparencies. Evolution of cloud boundaries are tracked using optical flow. Tracking extrapolation predicts future cloud movements and deformations as well as potential sun occlusions. To accurately estimate solar irradiance drop, we propose a novel scheme based on back-projecting the predicted sun occlusion onto the evolving cloud boundary to count for cloud transparency and irradiance drop estimation. Experimental results show that short-term solar irradiance drop is predictable with reasonable accuracy.