IDEAS home Printed from https://ideas.repec.org/h/spr/atlecp/978-94-6463-714-4_2.html

Project Solaris: Automated Progress Tracking of Solar Farms via Deep Learning

In: Proceedings of Sustainability, Entrepreneurship, Equity and Digital Strategies (SEEDS 2024)

Author

Listed:
  • Low Chun Kit

    (Sunway University)

  • Tan Hong Wei

    (Sunway University)

  • Cheah Gin Yang

    (Sunway University)

  • Asif Ali Bin Basheer Ali

    (Sunway University)

  • Simon Leroy Nicholas Pouponneau

    (Sunway University)

  • Narishah Mohamed Salleh

    (Sunway University)

  • Fathey Mohammed

    (Sunway University)

  • Ibrahim T. Nather Khasro

    (Sunway University)

  • Ahmed Khalid Mohd Khairi

    (Uzma Berhad)

Abstract

Solar energy has grown to become a key player for renewable energy in Malaysia poised for growth. The inherent issue that has come with such growth is the need to keep track of solar farm development. A fractured understanding of progress causes stakeholders being unable to make decisions with accurate information due to the manual tendencies hindering progress. Solving this issue no doubt can empower stakeholders with up-to-date information allowing for more decision making to be made early on, ensuring efficiencies are maintained. This study aims at automating the progress tracking of solar farms projects using deep learning. A seamless progress tracking ecosystem is developed by integrating deep learning with data visualization on a web-geo platform. The solution involves taking advantage of satellite imaging processing, image segmentation, data visualization techniques and data automation. This allows stakeholders to simplify the progress tracking and gain actionable insight without the need to visit farms physically. Ensuring this approach can revolutionize solar farm development tracking in Malaysia and transforming the decision-making process in its entirety moving forward.

Suggested Citation

  • Low Chun Kit & Tan Hong Wei & Cheah Gin Yang & Asif Ali Bin Basheer Ali & Simon Leroy Nicholas Pouponneau & Narishah Mohamed Salleh & Fathey Mohammed & Ibrahim T. Nather Khasro & Ahmed Khalid Mohd Kha, 2025. "Project Solaris: Automated Progress Tracking of Solar Farms via Deep Learning," Atlantis Highlights in Economics, Business and Management, in: Arpan Anand & Sreejith Balasubramanian (ed.), Proceedings of Sustainability, Entrepreneurship, Equity and Digital Strategies (SEEDS 2024), pages 4-19, Springer.
  • Handle: RePEc:spr:atlecp:978-94-6463-714-4_2
    DOI: 10.2991/978-94-6463-714-4_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:atlecp:978-94-6463-714-4_2. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.atlantis-press.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.