An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load

Peng Zhang; Chengke Zhou; Brian G. Stewart; Donald M. Hepburn; Wenjun Zhou; Jianhui Yu

doi:10.1016/j.egypro.2011.10.015

An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load

Peng Zhang^*, Chengke Zhou, Brian G. Stewart, Donald M. Hepburn, Wenjun Zhou, Jianhui Yu

^*Corresponding author for this work

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

24 Citations (Scopus)

24 Downloads (Pure)

Abstract

Non-intrusive load monitoring (NILM) is a convenient method to determine the electrical energy consumption and operation of individual appliances based on analysis of composite load measured at the entry of a building. It avoids installation of parallel sensors for monitoring individual appliances and could be applied in the smart metering system to obtain useful information for load management. This paper presents an improved NILM method that is capable of recognizing Electric Vehicle Battery (EVB) charging as a load type. Based on the proposed framework, a special pattern recognition algorithm is used to perform load disaggregation. A random switching simulator is developed to examine the performance of the improved NILM under various scenarios. The results demonstrate that the EVB charging load is recognized as well as other traditional appliances. The overall success rate of the disaggregation reaches 94.5% at typical circumstance. Through sensitivity analysis it is also shown that the EVB charging load makes a small impact on the overall performance.

Original language	English
Title of host publication	The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011
Editors	Qi Huang, Jason Z. Kang
Publisher	Elsevier B.V.
Pages	104-112
Number of pages	9
Volume	12
ISBN (Print)	9781627484138
DOIs	https://doi.org/10.1016/j.egypro.2011.10.015
Publication status	Published - 2011
Event	1st International Conference on Smart Grid and Clean Energy Technologies - Chengdu, China Duration: 27 Sept 2011 → 30 Sept 2011

Publication series

Name	Energy Procedia
Publisher	Elsevier B.V.
ISSN (Print)	1876-6102

Conference

Conference	1st International Conference on Smart Grid and Clean Energy Technologies
Abbreviated title	ICSGCE 2011
Country/Territory	China
City	Chengdu
Period	27/09/11 → 30/09/11

Keywords

Electric Vehicle Battery
Non-intrusive Load Monitoring
Pattern recognition
Smart metering

ASJC Scopus subject areas

General Energy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1016/j.egypro.2011.10.015

Final published versionFinal published version, 438 KBLicence: CC BY-NC-ND

Cite this

Zhang, P., Zhou, C., Stewart, B. G., Hepburn, D. M., Zhou, W., & Yu, J. (2011). An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load. In Q. Huang, & J. Z. Kang (Eds.), The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011 (Vol. 12, pp. 104-112). (Energy Procedia). Elsevier B.V.. https://doi.org/10.1016/j.egypro.2011.10.015

@inproceedings{ce942b601c624dc6818ff05dca2b4f98,

title = "An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load",

abstract = "Non-intrusive load monitoring (NILM) is a convenient method to determine the electrical energy consumption and operation of individual appliances based on analysis of composite load measured at the entry of a building. It avoids installation of parallel sensors for monitoring individual appliances and could be applied in the smart metering system to obtain useful information for load management. This paper presents an improved NILM method that is capable of recognizing Electric Vehicle Battery (EVB) charging as a load type. Based on the proposed framework, a special pattern recognition algorithm is used to perform load disaggregation. A random switching simulator is developed to examine the performance of the improved NILM under various scenarios. The results demonstrate that the EVB charging load is recognized as well as other traditional appliances. The overall success rate of the disaggregation reaches 94.5% at typical circumstance. Through sensitivity analysis it is also shown that the EVB charging load makes a small impact on the overall performance.",

keywords = "Electric Vehicle Battery, Non-intrusive Load Monitoring, Pattern recognition, Smart metering",

author = "Peng Zhang and Chengke Zhou and Stewart, {Brian G.} and Hepburn, {Donald M.} and Wenjun Zhou and Jianhui Yu",

note = "Funding Information: This work was supported in part by the Glasgow Caledonian University, Scotland, UK.; 1st International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2011 ; Conference date: 27-09-2011 Through 30-09-2011",

year = "2011",

doi = "10.1016/j.egypro.2011.10.015",

language = "English",

isbn = "9781627484138 ",

volume = "12",

series = "Energy Procedia",

publisher = "Elsevier B.V.",

pages = "104--112",

editor = "Qi Huang and Kang, {Jason Z.}",

booktitle = "The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011",

address = "Netherlands",

}

Zhang, P, Zhou, C, Stewart, BG, Hepburn, DM, Zhou, W & Yu, J 2011, An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load. in Q Huang & JZ Kang (eds), The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011. vol. 12, Energy Procedia, Elsevier B.V., pp. 104-112, 1st International Conference on Smart Grid and Clean Energy Technologies, Chengdu, China, 27/09/11. https://doi.org/10.1016/j.egypro.2011.10.015

An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load. / Zhang, Peng; Zhou, Chengke; Stewart, Brian G. et al.
The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011. ed. / Qi Huang; Jason Z. Kang. Vol. 12 Elsevier B.V., 2011. p. 104-112 (Energy Procedia).

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

TY - GEN

T1 - An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load

AU - Zhang, Peng

AU - Zhou, Chengke

AU - Stewart, Brian G.

AU - Hepburn, Donald M.

AU - Zhou, Wenjun

AU - Yu, Jianhui

N1 - Funding Information: This work was supported in part by the Glasgow Caledonian University, Scotland, UK.

PY - 2011

Y1 - 2011

N2 - Non-intrusive load monitoring (NILM) is a convenient method to determine the electrical energy consumption and operation of individual appliances based on analysis of composite load measured at the entry of a building. It avoids installation of parallel sensors for monitoring individual appliances and could be applied in the smart metering system to obtain useful information for load management. This paper presents an improved NILM method that is capable of recognizing Electric Vehicle Battery (EVB) charging as a load type. Based on the proposed framework, a special pattern recognition algorithm is used to perform load disaggregation. A random switching simulator is developed to examine the performance of the improved NILM under various scenarios. The results demonstrate that the EVB charging load is recognized as well as other traditional appliances. The overall success rate of the disaggregation reaches 94.5% at typical circumstance. Through sensitivity analysis it is also shown that the EVB charging load makes a small impact on the overall performance.

AB - Non-intrusive load monitoring (NILM) is a convenient method to determine the electrical energy consumption and operation of individual appliances based on analysis of composite load measured at the entry of a building. It avoids installation of parallel sensors for monitoring individual appliances and could be applied in the smart metering system to obtain useful information for load management. This paper presents an improved NILM method that is capable of recognizing Electric Vehicle Battery (EVB) charging as a load type. Based on the proposed framework, a special pattern recognition algorithm is used to perform load disaggregation. A random switching simulator is developed to examine the performance of the improved NILM under various scenarios. The results demonstrate that the EVB charging load is recognized as well as other traditional appliances. The overall success rate of the disaggregation reaches 94.5% at typical circumstance. Through sensitivity analysis it is also shown that the EVB charging load makes a small impact on the overall performance.

KW - Electric Vehicle Battery

KW - Non-intrusive Load Monitoring

KW - Pattern recognition

KW - Smart metering

U2 - 10.1016/j.egypro.2011.10.015

DO - 10.1016/j.egypro.2011.10.015

M3 - Conference contribution

AN - SCOPUS:84555209201

SN - 9781627484138

VL - 12

T3 - Energy Procedia

SP - 104

EP - 112

BT - The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011

A2 - Huang, Qi

A2 - Kang, Jason Z.

PB - Elsevier B.V.

T2 - 1st International Conference on Smart Grid and Clean Energy Technologies

Y2 - 27 September 2011 through 30 September 2011

ER -

Zhang P, Zhou C, Stewart BG, Hepburn DM, Zhou W, Yu J. An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load. In Huang Q, Kang JZ, editors, The Proceedings of International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2011. Vol. 12. Elsevier B.V. 2011. p. 104-112. (Energy Procedia). Epub 2011 Dec 15. doi: 10.1016/j.egypro.2011.10.015

An improved non-intrusive load monitoring method for recognition of Electric Vehicle Battery charging load

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

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Cite this