Accurately Forecasting the Health of Energy System Assets

Wenshuo Tang; Merlinda Andoni; Valentin Robu; David Flynn

doi:10.1109/ISCAS.2018.8351842

Accurately Forecasting the Health of Energy System Assets

Wenshuo Tang, Merlinda Andoni, Valentin Robu, David Flynn

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

6 Citations (Scopus)

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 language	English
Title of host publication	2018 IEEE International Symposium on Circuits and Systems (ISCAS)
Publisher	IEEE
ISBN (Electronic)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8351842
Publication status	Published - 4 May 2018
Event	2018 IEEE International Symposium on Circuits and Systems - Florence Congress Centre, Florence, Italy Duration: 27 May 2018 → 30 May 2018 http://www.iscas2018.org/

Publication series

Name	International Symposium on Circuits and Systems (ISCAS)
Publisher	IEEE
ISSN (Electronic)	2379-447X

Conference

Conference	2018 IEEE International Symposium on Circuits and Systems
Abbreviated title	ISCAS 2018
Country/Territory	Italy
City	Florence
Period	27/05/18 → 30/05/18
Internet address	http://www.iscas2018.org/

Keywords

Energy Systems
Asset management
Prognostics
Data analysis
Storage

Access to Document

10.1109/ISCAS.2018.8351842

Cite this

@inproceedings{eecb7a77cb8c4db9be9c4ec4c0d956e4,

title = "Accurately Forecasting the Health of Energy System Assets",

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 andsustainable energy systems.",

keywords = "Energy Systems, Asset management, Prognostics, Data analysis, Storage",

author = "Wenshuo Tang and Merlinda Andoni and Valentin Robu and David Flynn",

year = "2018",

month = may,

day = "4",

doi = "10.1109/ISCAS.2018.8351842",

language = "English",

series = "International Symposium on Circuits and Systems (ISCAS)",

publisher = "IEEE",

booktitle = "2018 IEEE International Symposium on Circuits and Systems (ISCAS)",

address = "United States",

note = "2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 ; Conference date: 27-05-2018 Through 30-05-2018",

url = "http://www.iscas2018.org/",

}

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, 2018 IEEE International Symposium on Circuits and Systems, Florence, Italy, 27/05/18. https://doi.org/10.1109/ISCAS.2018.8351842

TY - GEN

T1 - Accurately Forecasting the Health of Energy System Assets

AU - Tang, Wenshuo

AU - Andoni, Merlinda

AU - Robu, Valentin

AU - Flynn, David

PY - 2018/5/4

Y1 - 2018/5/4

N2 - 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 andsustainable energy systems.

AB - 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 andsustainable energy systems.

KW - Energy Systems

KW - Asset management

KW - Prognostics

KW - Data analysis

KW - Storage

U2 - 10.1109/ISCAS.2018.8351842

DO - 10.1109/ISCAS.2018.8351842

M3 - Conference contribution

T3 - International Symposium on Circuits and Systems (ISCAS)

BT - 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

PB - IEEE

T2 - 2018 IEEE International Symposium on Circuits and Systems

Y2 - 27 May 2018 through 30 May 2018

ER -

Accurately Forecasting the Health of Energy System Assets

Abstract

Publication series

Conference

Keywords

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