Smart grid data analysis and prediction modeling

Hang Yang; Ping Li; Anum Masood; Yuning Xiao; Bin Sheng; Qichen Yu

doi:10.1109/PIC.2016.7949559

Smart grid data analysis and prediction modeling

Hang Yang
, Ping Li
, Anum Masood
, Yuning Xiao
, Bin Sheng
, Qichen Yu

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

2 Citations (Scopus)

Abstract

With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.

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	541-544
Number of pages	4
ISBN (Electronic)	9781509034833
DOIs	https://doi.org/10.1109/PIC.2016.7949559
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
HBase data storage
Machine learning
Power prediction
SVM

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.2016.7949559

Cite this

Yang, H., Li, P., Masood, A., Xiao, Y., Sheng, B., & Yu, Q. (2016). Smart grid data analysis and prediction modeling. In Y. Wang, & Y. Sun (Eds.), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 541-544). Article 7949559 (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.2016.7949559

Yang, Hang ; Li, Ping ; Masood, Anum et al. / Smart grid data analysis and prediction modeling. 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. 541-544 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing).

@inproceedings{1f5779aaef6b4b35a3d82cb71300f04b,

title = "Smart grid data analysis and prediction modeling",

abstract = "With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.",

keywords = "Data mining, HBase data storage, Machine learning, Power prediction, SVM",

author = "Hang Yang and Ping Li and Anum Masood and Yuning Xiao and Bin Sheng and Qichen Yu",

year = "2016",

month = dec,

doi = "10.1109/PIC.2016.7949559",

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 = "541--544",

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",

}

Yang, H, Li, P, Masood, A, Xiao, Y, Sheng, B & Yu, Q 2016, Smart grid data analysis and prediction modeling. in Y Wang & Y Sun (eds), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing., 7949559, PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing, Institute of Electrical and Electronics Engineers Inc., pp. 541-544, 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016, Shanghai, China, 23/12/16. https://doi.org/10.1109/PIC.2016.7949559

Smart grid data analysis and prediction modeling. / Yang, Hang; Li, Ping; Masood, Anum 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. 541-544 7949559 (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 analysis and prediction modeling

AU - Yang, Hang

AU - Li, Ping

AU - Masood, Anum

AU - Xiao, Yuning

AU - Sheng, Bin

AU - Yu, Qichen

PY - 2016/12

Y1 - 2016/12

N2 - With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.

AB - With the rapiddevelopment of power grid, prediction ofelectric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidityhardly have any effects.

KW - Data mining

KW - HBase data storage

KW - Machine learning

KW - Power prediction

KW - SVM

UR - https://www.scopus.com/pages/publications/85025156554

U2 - 10.1109/PIC.2016.7949559

DO - 10.1109/PIC.2016.7949559

M3 - Conference article published in proceeding or book

AN - SCOPUS:85025156554

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

SP - 541

EP - 544

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 -

Yang H, Li P, Masood A, Xiao Y, Sheng B, Yu Q. Smart grid data analysis and prediction modeling. 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. 541-544. 7949559. (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing). doi: 10.1109/PIC.2016.7949559

Smart grid data analysis and prediction modeling

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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