Smart grid data analysis and prediction modeling

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

Research output: Chapter in Book/Report/Conference proceedingChapters

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 languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
Place of PublicationBeijing
PublisherInstitute of Electrical and Electronics Engineers, Inc
Pages541-544
ISBN (Print)9781509034840, 9781509034833
Publication statusPublished - 2016

Citation

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.), Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 541-544). Beijing: Institute of Electrical and Electronics Engineers, Inc.

Keywords

  • Power prediction
  • Data mining
  • HBase data storage
  • Machine learning
  • SVM

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