Smart grid data analysis and prediction modeling

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

Research output: Contribution to conferencePapers


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.
Original languageEnglish
Publication statusPublished - Dec 2016


Yang, H., Li, P., Masood, A., Xiao, Y., Sheng, B., & Yu, Q. (2016, December). Smart grid data analysis and prediction modeling. Paper presented at The 2016 IEEE International Conference on Progress in Informatics and Computing (PIC-2016), Shanghai Guang Dong Hotel, Shanghai, China.


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


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