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Research on Parallel Forecasting Model of Short-Term Power Load Big Data

  • Xin-jia LiEmail author
  • Hong Sun
  • Cheng-liang Wang
  • Si-yu Tao
  • Tao Lei
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)

Abstract

The parallel prediction model of big data with traditional power load has a low prediction accuracy in different working conditions, so the parallel prediction model of big data for short-term power load is designed. The short-term power load forecasting theory is analyzed, and the short-term power load data are classified to select the short-term power load forecasting theory. The Map/Reduce framework is built on the basis of the theory, and the prediction process is designed through the Map/Reduce framework. The short-term power load data of the subnet and the big data of the short term power load are predicted respectively, and the construction of the parallel prediction model of the short-term power load big data is realized. The experimental results show that the proposed big data parallel prediction model is better than the traditional model, and can be switched under different working conditions, and the deviation between the forecasting curve and the actual load is small, the average deviation is 1.7, and the overall prediction effect is good.

Keywords

Short-Term load forecasting Big data Electrical load Prediction algorithm 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Xin-jia Li
    • 1
    Email author
  • Hong Sun
    • 1
  • Cheng-liang Wang
    • 1
  • Si-yu Tao
    • 2
  • Tao Lei
    • 3
  1. 1.Jiangsu Fangtian Power Technology Co., Ltd.NanjingChina
  2. 2.Southeast UniversityNanjingChina
  3. 3.South China Normal UniversityGuangzhouChina

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