Smart grid data mining and visualization

Yingyao ZHOU, Ping LI, Yuning XIAO, Anum MASOOD, Qichen YU, Bin SHENG

Research output: Contribution to conferencePapers


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 languageEnglish
Publication statusPublished - Dec 2016


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


  • Smart grid
  • Data mining
  • Visualization
  • Geographic information systems
  • Distributed computing


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