Sustainable energies and machine learning: An organized review of recent applications and challenges

Pouya Ifaei, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, Chang Kyoo Yoo

Research output: Contribution to journalReview articlepeer-review

2 Citations (SciVal)


In alignment with the rapid development of artificial intelligence in the era of data management, the application domains for machine learning have expanded to all engineering fields. Noting the importance of using sustainable energies to run the world for the rest of this century, much research has focused on applying machine learning techniques to renewable and sustainable energies, and a comprehensive, well-organized, reader-oriented review of that research is needed. This review organizes the essential basics, major applications, and remaining challenges of machine learning in sustainable energies into two parts to accommodate the background knowledge and interests of both artificial intelligence and sustainable energy experts. First, the major applications of machine learning are divided into prediction, clustering, and optimization. For each category, the literature is categorized from new viewpoints, and research trends are highlighted to focus future research. In the second part, three primary machine learning–driven sustainability areas are introduced with respect to their abundance in the literature: multi-carrier energy systems, spatiotemporal analytics, and circular integration. For each engineering area, the contemporary progress is investigated in terms of a specific future research path. It is anticipated that the rapid application of machine learning tools will speed the development of sustainable energies during the next decade.

Original languageEnglish (US)
Article number126432
StatePublished - Mar 1 2023

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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