Smart grid data mining and visualization

Yingyao Zhou; Ping Li; Yuning Xiao; Anum Masood; Qichen Yu; Bin Sheng

doi:10.1109/PIC.2016.7949558

Smart grid data mining and visualization

Yingyao Zhou
, Ping Li
, Yuning Xiao
, Anum Masood
, Qichen Yu
, Bin Sheng

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

The power industry innovation has increasingly become a top concern for current reforms. Power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.

Original language	English
Title of host publication	PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
Editors	Yinglin Wang, Yaoru Sun
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	536-540
Number of pages	5
ISBN (Electronic)	9781509034833
DOIs	https://doi.org/10.1109/PIC.2016.7949558
Publication status	Published - Dec 2016
Externally published	Yes
Event	4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 - Shanghai, China Duration: 23 Dec 2016 → 25 Dec 2016

Publication series

Name	PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

Conference

Conference	4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016
Country/Territory	China
City	Shanghai
Period	23/12/16 → 25/12/16

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Data mining
Distributed computing
Geographic information systems
Smart grid
Visualization

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Health Informatics

More information

10.1109/PIC.2016.7949558

Cite this

Zhou, Y., Li, P., Xiao, Y., Masood, A., Yu, Q., & Sheng, B. (2016). Smart grid data mining and visualization. In Y. Wang, & Y. Sun (Eds.), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 536-540). Article 7949558 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PIC.2016.7949558

Zhou, Yingyao ; Li, Ping ; Xiao, Yuning et al. / Smart grid data mining and visualization. PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. editor / Yinglin Wang ; Yaoru Sun. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 536-540 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing).

@inproceedings{1fd9bcaec7934193b331d726d691a8cb,

title = "Smart grid data mining and visualization",

abstract = "The power industry innovation has increasingly become a top concern for current reforms. Power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.",

keywords = "Data mining, Distributed computing, Geographic information systems, Smart grid, Visualization",

author = "Yingyao Zhou and Ping Li and Yuning Xiao and Anum Masood and Qichen Yu and Bin Sheng",

year = "2016",

month = dec,

doi = "10.1109/PIC.2016.7949558",

language = "English",

series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "536--540",

editor = "Yinglin Wang and Yaoru Sun",

booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",

note = "4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",

}

Zhou, Y, Li, P, Xiao, Y, Masood, A, Yu, Q & Sheng, B 2016, Smart grid data mining and visualization. in Y Wang & Y Sun (eds), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing., 7949558, PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing, Institute of Electrical and Electronics Engineers Inc., pp. 536-540, 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016, Shanghai, China, 23/12/16. https://doi.org/10.1109/PIC.2016.7949558

Smart grid data mining and visualization. / Zhou, Yingyao; Li, Ping; Xiao, Yuning et al.
PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. ed. / Yinglin Wang; Yaoru Sun. Institute of Electrical and Electronics Engineers Inc., 2016. p. 536-540 7949558 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Smart grid data mining and visualization

AU - Zhou, Yingyao

AU - Li, Ping

AU - Xiao, Yuning

AU - Masood, Anum

AU - Yu, Qichen

AU - Sheng, Bin

PY - 2016/12

Y1 - 2016/12

N2 - The power industry innovation has increasingly become a top concern for current reforms. Power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.

AB - The power industry innovation has increasingly become a top concern for current reforms. Power systems feature scattered data storage, incapable data analysis ability, poor computing capability, and ineffective interaction interface. To resolve these issues, we need multiple data mining techniques to extract information for analytical capacity improvement. Secondly, we need visualization techniques to analyze and optimize interaction. Lastly, we need distributed technologies for unified data management to increase computing capability and system scalability. Considering China's smart grid information, this paper proposes solutions to problems, such as the existing underdeveloped power management systems, a lack of automation methods, low data visualization, and poor data management. The electric power industry has functional requirements for this research. Based on existing data mining, visualization and understanding of distributed technologies, we discussed the functions of each part of the implementation in a smart grid management system: the data mining module, visualization module and data management module.

KW - Data mining

KW - Distributed computing

KW - Geographic information systems

KW - Smart grid

KW - Visualization

UR - https://www.scopus.com/pages/publications/85025126636

U2 - 10.1109/PIC.2016.7949558

DO - 10.1109/PIC.2016.7949558

M3 - Conference article published in proceeding or book

AN - SCOPUS:85025126636

T3 - PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

SP - 536

EP - 540

BT - PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

A2 - Wang, Yinglin

A2 - Sun, Yaoru

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016

Y2 - 23 December 2016 through 25 December 2016

ER -

Zhou Y, Li P, Xiao Y, Masood A, Yu Q, Sheng B. Smart grid data mining and visualization. In Wang Y, Sun Y, editors, PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. Institute of Electrical and Electronics Engineers Inc. 2016. p. 536-540. 7949558. (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing). doi: 10.1109/PIC.2016.7949558

Smart grid data mining and visualization

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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