JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (6): 52-56.doi: 10.6040/j.issn.1672-3961.0.2017.376

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Short-term power load forecasting based on support vector regression

LI Sun1, WANG Chao2, ZHANG Guilin3*, XU Zhigen2, CHENG Tao2, WANG Yiyuan2, WANG Ruiqi1   

  1. 1. State Grid Shandong Electric Power Company, Jinan 250001, Shandong, China;
    2. Qingdao Power Supply Company, State Grid Shandong Electric Power Company, Qingdao 266002, Shandong, China;
    3. School of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Received:2017-08-03 Online:2017-12-20 Published:2017-08-03

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Abstract: The characters of short term load were studied and the influence factors of daily load in summer and winter was analysed. The meteorological factors, such as date type and pevious load, were selected as the input of maximum incremental load forecasting regression model. The value corresponding to date type was modified based on meteorological factor due to the inconsistent load characteristic in different seasons. The least squares support vector machine(LS-SVM)was utilized to model mapping relationship between input factors and maximum incremental load. Numerical tests demonstrated the efficiency of the proposed method.

Key words: similar days, support vector regression(SVR), load forecasting, peak load

CLC Number: 

  • TM715
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