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Feature Evaluation in Machine Learning-Based Soc Estimation for PV Battery Systems

  • University of Rhode Island’s College of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original languageEnglish
Title of host publicationConference Proceedings - 2025 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331576400
DOIs
StatePublished - 2025
Externally publishedYes
Event2025 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2025 - Wollongong, Australia
Duration: Dec 7 2025 → Dec 11 2025

Publication series

SeriesConference Proceedings - 2025 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2025

Conference

Conference2025 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2025
Country/TerritoryAustralia
CityWollongong
Period12/7/25 → 12/11/25

ASJC Scopus Subject Areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

Keywords

  • battery
  • convolutional neural network
  • feature engineering
  • machine learning
  • permutation feature importance
  • state of charge

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