Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques

Ajith Gopi, Prabhakar Sharma, Kumarasamy Sudhakar*, Wai Keng Ngui, Irina Kirpichnikova, Erdem Cuce

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    58 Citations (SciVal)
    Original languageEnglish
    Article number439
    JournalSustainability (Switzerland)
    Volume15
    Issue number1
    DOIs
    Publication statusPublished (VoR) - Jan 2023

    Funding

    The authors are grateful for the financial support of the Universiti Malaysia Pahang (www.ump.edu.my) through grant PGRS210349. The authors are also thankful to the Russian Science Foundation grant no. 22-19-20011 support of South Ural state university.

    FundersFunder number
    South Ural State University
    Universiti Malaysia PahangPGRS210349
    Russian Science Foundation22-19-20011

      Keywords

      • artificial intelligence
      • energy generation
      • forecasting
      • neuro-fuzzy
      • solar irradiance
      • solar plant

      Fingerprint

      Dive into the research topics of 'Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques'. Together they form a unique fingerprint.

      Cite this