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

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 and Ahmed Khalid Mohd Khairi
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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

A chapter in Proceedings of Sustainability, Entrepreneurship, Equity and Digital Strategies (SEEDS 2024), 2025, pp 4-19 from Springer

Abstract: 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.

Keywords: API; CNN; Deep learning (DL); Solar energy; KPI; MLOps; tracking system (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-714-4_2

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