Intra-Hour Cloud Tracking Based on Probability Hypothesis Density Filtering

Abstract

IEEE Swift variations in the cloud cover may cause significant power output fluctuations in solar power systems, jeopardizing power quality. Forecasting the power output with a very short horizon below 30 seconds can be seen as a cloud cover forecasting problem solved by processing images from a sky camera. After a pre-processing stage that identifies clouds with a criterion based on the red-green blue (RGB) values of each pixel, a Probability Hypothesis Density (PHD) filter or a more advanced Cardinalized Probability Hypothesis Density (CPHD) filter can be used to an unknown and varying number of clouds. The time when cumulus clouds will shade the sun can be forecasted with an absolute precision 6 seconds ahead and with an acceptable accuracy 27 seconds ahead. It has been found that both filters are equally well-suited for a real-time online nowcasting application. They also have the potential to deal with noise and the swift dynamics and high variability of clouds.