Short term prediction of sun coverage using optical flow with GoogLeNet
Nithiphat Teerakawanich, Thanonchai Leelaruji, Achara Pichetjamroen
Abstract
One of the challenges of PV power generation is solar intermittency from weather conditions. Solar irradiance prediction is therefore required to deal with this issue. Several prediction methods have been proposed based on whole sky image processing techniques. This paper presents a combination technique of image processing with a convolution neural network (CNN) based on GoogLeNet for raising trigger events before the sun cover happens 1 to 2 min in advance. The captured sky images are preprocessed and in the next step, we use Hough transform to find the sun position and use optical flow to track cloud movement. Finally, we use a CNN to generate trigger events in advance before the sun occlusion happens. The results of prediction stage show error percentage as low as 5.26% in a clear sky day.