Smart grid data analysis and prediction modeling

Hang YANG; Ping LI; Anum MASOOD; Yuning XIAO; Bin SHENG; Qichen YU

Smart grid data analysis and prediction modeling

Hang YANG
, Ping LI
, Anum MASOOD
, Yuning XIAO
, Bin SHENG
, Qichen YU

Department of Mathematics and Information Technology (MIT)

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

Abstract

With the rapid development of power grid, prediction of electric 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 humidity hardly have any effects. Copyright © 2016 IEEE.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
Place of Publication	Beijing
Publisher	Institute of Electrical and Electronics Engineers, Inc
Pages	541-544
ISBN (Print)	9781509034840, 9781509034833
Publication status	Published - 2016

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Power prediction
Data mining
HBase data storage
Machine learning
SVM

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

abstract = "With the rapid development of power grid, prediction of electric 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 humidity hardly have any effects. Copyright {\textcopyright} 2016 IEEE.",

author = "Hang YANG and Ping LI and Anum MASOOD and Yuning XIAO and Bin SHENG and Qichen YU",

year = "2016",

language = "English",

isbn = "9781509034840",

pages = "541--544",

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

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

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TY - CHAP

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

Y1 - 2016

N2 - With the rapid development of power grid, prediction of electric 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 humidity hardly have any effects. Copyright © 2016 IEEE.

AB - With the rapid development of power grid, prediction of electric 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 humidity hardly have any effects. Copyright © 2016 IEEE.

M3 - Chapters

SN - 9781509034840

SN - 9781509034833

SP - 541

EP - 544

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

PB - Institute of Electrical and Electronics Engineers, Inc

CY - Beijing

ER -

Smart grid data analysis and prediction modeling

Abstract

UN SDGs

Keywords

Fingerprint