Prediction of HSV color model parameter values of cloud movement picture based on artificial neural networks

In order to predict the exact moment of Sun shading by clouds and Sun cover duration to optimize the energy flow in the microgrid with solar photo electric system, it is essential to transform cloud images from RGB color model into HSV color model to be able to precisely detect cloud edges and determine the position of centroids for prediction of cloud movements. Parameters that define the quality of the image depend on the range of values for Hue, Saturation and Value (HSV) components. The dynamics of clouds and changing their shapes, sizes and colors require constant adjustments of those parameters by a human to get the best results. This paper deals with prediction and automatic setting of the HSV parameters by using artificial neural network and supervised learning. The image processing and parameters prediction was performed by an application developed in Java programming language based on JavaCV library and Encog framework for implementation of the artificial neural network.