Enhancing solar energy forecasting using artificial intelligence for emergent plants with limited historical data

Enhancing solar energy forecasting using artificial intelligence for emergent plants with limited historical data

This project aims to provide an efficient and robust method for solar power generation predictions for newly established farms that have few or no historical data.

By leveraging data from established solar plants and employing Artificial Intelligence (AI) algorithms, the method will extrapolate and adapt patterns from these data-rich environments to make accurate predictions for the new solar plant.

Dates (start - end)

January 2023 to December 2023

Project outcomes

  • Accurate prediction models: Establishment of AI-driven prediction models that reliably forecast solar power output for farms with limited historical data.
  • Data utilisation: Effective use of existing data from established solar power plants to supplement the lack of data in newly established farms.
  • Enhanced operational efficiency: Improvement in operational planning and resource allocation for new solar farms based on predictive insights.
  • Scalable framework: Creation of a scalable framework that can be adapted to various geographic and climatic conditions, thereby benefiting a wider range of solar power projects.
  • Sustainability impact: Contributing to the sustainability goals by improving the efficiency and stability of renewable energy sources.

Key people

Funding source

  • Sarah Almaghrabi is supported by a scholarship from Jeddah University, Saudi Arabia.
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Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.

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aboriginal flag
torres strait flag

Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business.

More information