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Ascertaining sustainability for affordable energy generation with non-renewable sources using computational

Adil O Khadidos1, Hariprasath Manoharan2, Alaa O Khadidos3,4

  • 1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

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This summary is machine-generated.

Achieving affordable and sustainable energy in India requires reducing demand and increasing generation. Computational intelligence methods can help analyze non-renewable sources, potentially reducing fossil fuel impact by over 80%.

Keywords:
Affordable energyComputational intelligenceDemand and generationEnvironmental researchSustainability

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Area of Science:

  • Energy Systems
  • Computational Intelligence
  • Sustainability Studies

Background:

  • Growing populations necessitate sustainable and affordable electricity in India.
  • Reducing energy demand and increasing generation are crucial for energy sustainability.

Purpose of the Study:

  • To analyze the role of non-renewable energy sources for sustainable electricity generation in India.
  • To investigate the potential of computational intelligence for optimizing energy availability and reducing costs.

Main Methods:

  • Utilized computational intelligence algorithms to analyze non-renewable energy sources.
  • Employed clustering techniques to identify regional sustainability and connectivity.
  • Compared four scenarios to evaluate reductions in fossil fuel impact.

Main Results:

  • Computational intelligence effectively identifies current energy demands and potential reductions.
  • The proposed method demonstrates the possibility of reducing energy demand by over 80% through optimized generation.
  • Analysis indicates a significant reduction in the impact of fossil fuels through the alternate use of non-renewable sources.

Conclusions:

  • Computational intelligence offers a viable approach to achieving affordable and sustainable energy targets in India.
  • Optimized energy generation and reduced demand can significantly lower costs and environmental impact.
  • Regional clustering and connectivity enhance the efficiency of sustainable energy solutions.