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

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

Authors
Low Chun Kit1, *, Tan Hong Wei1, Cheah Gin Yang1, Asif Ali Bin Basheer Ali1, Simon Leroy Nicholas Pouponneau1, Narishah Mohamed Salleh1, Fathey Mohammed1, Ibrahim T. Nather Khasro1, Ahmed Khalid Mohd Khairi2
1Sunway University, Selangor, Malaysia
2Uzma Berhad, Petaling Jaya, Malaysia
*Corresponding author.
Corresponding Author
Low Chun Kit
Available Online 5 May 2025.
DOI
10.2991/978-94-6463-714-4_2How to use a DOI?
Keywords
API; CNN; Deep learning (DL); Solar energy; KPI; MLOps; tracking system
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.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of Sustainability, Entrepreneurship, Equity and Digital Strategies (SEEDS 2024)
Series
Atlantis Highlights in Economics, Business and Management
Publication Date
5 May 2025
ISBN
978-94-6463-714-4
ISSN
2667-1271
DOI
10.2991/978-94-6463-714-4_2How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Low Chun Kit
AU  - Tan Hong Wei
AU  - Cheah Gin Yang
AU  - Asif Ali Bin Basheer Ali
AU  - Simon Leroy Nicholas Pouponneau
AU  - Narishah Mohamed Salleh
AU  - Fathey Mohammed
AU  - Ibrahim T. Nather Khasro
AU  - Ahmed Khalid Mohd Khairi
PY  - 2025
DA  - 2025/05/05
TI  - Project Solaris: Automated Progress Tracking of Solar Farms via Deep Learning
BT  - Proceedings of Sustainability, Entrepreneurship, Equity and Digital Strategies (SEEDS 2024)
PB  - Atlantis Press
SP  - 4
EP  - 19
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-714-4_2
DO  - 10.2991/978-94-6463-714-4_2
ID  - Kit2025
ER  -