Tang, W., Andoni, M. , Robu, V. and Flynn, D. (2018) Accurately Forecasting the Health of Energy System Assets. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 27-30 May 2018, ISBN 9781538648810 (doi: 10.1109/ISCAS.2018.8351842)
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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.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Andoni, Dr Merlinda and Flynn, Professor David and Tang, Mr Wenshuo |
| Authors: | Tang, W., Andoni, M., Robu, V., and Flynn, D. |
| College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | Proceedings - IEEE International Symposium on Circuits and Systems |
| ISSN: | 2379-447X |
| ISBN: | 9781538648810 |
| Related URLs: |
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