|Autors: Hensel S., Marinov, M. B., Koch M., Arnaudov, D. D.|
Title: Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
Keywords: machine learning, generation, ground-based sky image, irradiation, load scheduling, photovoltaic power, short-term forecasting, solar irradiance, solar photovoltaics, total cloud cover
Abstract: This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus