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Research on Short-Term Prediction of Power Grid Status Data Based on SVM

  • Jianjun Su
  • Yi Yang
  • Danfeng Yan
  • Ye TangEmail author
  • Zongqi Mu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

EMS (Energy management system) is a collection of computer hardware and software, which collects, monitors, controls and optimizes data provided by power control system, and provide trading scheme, security services and service analysis for power market. The prediction of status data is a basic function module of advanced application software systems. Therefore it is meaningful to do research on new method and new technology of predicting power grid status data. In this paper, support vector machine is used to do regression prediction for active power of EMS. In training process, the training set and kernel function of SVM are selected, and parameters are optimized, also, the performance of SVM is evaluated. Experiments show that SVM can get higher accuracy in short term active power prediction although the data set is small. This paper provides a new idea for related research works in electric power industry system.

Keywords

EMS SVM Regression prediction Parameter optimization Machine learning 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Jianjun Su
    • 1
  • Yi Yang
    • 1
  • Danfeng Yan
    • 2
  • Ye Tang
    • 2
    Email author
  • Zongqi Mu
    • 2
  1. 1.State Grid ShanDong Electric Power Research InstituteJinanChina
  2. 2.Beijing University of Posts and TelecommunicationsBeijingChina

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