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Study on Short-Term Load Forecasting Considering Meteorological Similar Days

  • Zhaojun Lu
  • Zhijie Zheng
  • Hongwei Wang
  • Yajing Gao
  • Yuwei Lei
  • Mingrui ZhaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

A short-term load forecasting method based on meteorological similar days and error correction is proposed in this paper. First, SPSS software is used to carry on the regression analysis of meteorological factors, select most significant meteorological factors in each season, and determine the weight of each factor as the basis for selecting the weather similar days. Then the historical forecast error data sample set is set up. For a certain forecast date, the error data samples from the similar days are extracted to establish a set, and the probability density distribution model is established. Finally, the error fluctuation of the forecast point is analyzed to get the compensated value of forecast error. The sampling error closest to the error compensation value is selected as the fitted error values and added to the predicted value to improve the forecast accuracy.

Keywords

Meteorological similar days Probability density distribution Error correction Volatility analysis 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhaojun Lu
    • 1
  • Zhijie Zheng
    • 2
  • Hongwei Wang
    • 1
  • Yajing Gao
    • 3
  • Yuwei Lei
    • 3
  • Mingrui Zhao
    • 3
    Email author
  1. 1.State Grid Shandong Electric Power CompanyJinanChina
  2. 2.State Grid Shandong Electric Power CompanyEconomic & Technological Research InstituteJinanChina
  3. 3.CEC Electric Power Development Research InstituteBeijingChina

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