An improved cloud recognition and classification method for photovoltaic power prediction based on total-sky-images

Abstract

The rapid variation of irradiance due to cloud movements and occlusions is the main factor causing the fluctuation of photovoltaic (PV) power generation. Accurate recognition and classification of cloud is the prerequisite of improving the prediction accuracy of irradiance. Total-Sky-Images (TSIs) taken by ground-based cameras are a good solution to analyse the distribution of cloud in real-time and is suitable for ultra-short-term PV power prediction. Images are often processed by threshold-based segmentation algorithm, which is based on the brightness or greyscale value of pixels. However, the sunlight can change the brightness or greyscale distribution of those pixels which represent cloud and cause misclassification. This paper proposes a novel cloud classification method based on greyscale compensation values (GCVs) and Otsu algorithm to solve this problem and divides the total sky into three parts: clear sky, thin cloud and thick cloud. GCVs are greyscale values which extract from clear-sky TSIs. Experimental results show that this method can effectively improve the accuracy of cloud classification.

Keywords

• image segmentation
• geophysical image processing
• sunlight
• atmospheric techniques
• image classification
• clouds
• photovoltaic power systems
• ground-based cameras
• good solution
• ultra-short-term pv power prediction
• threshold-based segmentation algorithm
• brightness
• greyscale value
• pixels
• greyscale distribution
• cause misclassification
• novel cloud classification method
• total sky
• clear sky
• greyscale values
• clear-sky tsis
• improved cloud recognition
• photovoltaic power prediction
• total-sky-images
• cloud movements
• main factor
• photovoltaic power generation
• prediction accuracy