Accurately Forecasting the Health of Energy System Assets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present a review into data driven prognostics and its relevance to resilience in energy systems. A data driven remaining useful life prediction for Li-ion batteries utilizing data analysis via a relevance vector machine (RVM) model is shown to be within 5% accuracy when applied to large lifecycle datasets. Results demonstrate that due to the agile nature of prognostic models and their accuracy, prognostics and health management methods will be vital to resilient and
sustainable energy systems.
Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
ISBN (Electronic)9781538648810
DOIs
Publication statusPublished - 4 May 2018
Event2018 IEEE International Symposium on Circuits and Systems - Florence Congress Centre, Florence, Italy
Duration: 27 May 201830 May 2018
http://www.iscas2018.org/

Publication series

NameInternational Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
ISSN (Electronic)2379-447X

Conference

Conference2018 IEEE International Symposium on Circuits and Systems
Abbreviated titleISCAS 2018
CountryItaly
CityFlorence
Period27/05/1830/05/18
Internet address

Keywords

  • Energy Systems
  • Asset management
  • Prognostics
  • Data analysis
  • Storage

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  • Cite this

    Tang, W., Andoni, M., Robu, V., & Flynn, D. (2018). Accurately Forecasting the Health of Energy System Assets. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS) [8351842] (International Symposium on Circuits and Systems (ISCAS)). IEEE. https://doi.org/10.1109/ISCAS.2018.8351842