IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6156-d644194.html
   My bibliography  Save this article

Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

Author

Listed:
  • Stefan Hensel

    (Department for Electrical Engineering, University of Applied Sciences Offenburg, Badstraße 24, D-77652 Offenburg, Germany)

  • Marin B. Marinov

    (Department of Electronics, Technical University of Sofia, 8, Kliment Ohridski Blvd., BG-1756 Sofia, Bulgaria)

  • Michael Koch

    (Department for Electrical Engineering, University of Applied Sciences Offenburg, Badstraße 24, D-77652 Offenburg, Germany)

  • Dimitar Arnaudov

    (Department of Power Electronics, Technical University of Sofia, 8, Kliment Ohridski Blvd., BG-1756 Sofia, Bulgaria)

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.

Suggested Citation

  • Stefan Hensel & Marin B. Marinov & Michael Koch & Dimitar Arnaudov, 2021. "Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation," Energies, MDPI, vol. 14(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6156-:d:644194
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Minsu Kim & Hongmyeong Kim & Jae Hak Jung, 2021. "A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules," Energies, MDPI, vol. 14(5), pages 1-11, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xuxu Li & Xiaojiang Liu & Yun Xiao & Yao Zhang & Xiaomei Yang & Wenhai Zhang, 2022. "An Improved U-Net Segmentation Model That Integrates a Dual Attention Mechanism and a Residual Network for Transformer Oil Leakage Detection," Energies, MDPI, vol. 15(12), pages 1-15, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6156-:d:644194. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.