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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 52-56.doi: 10.6040/j.issn.1672-3961.0.2017.376

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基于支持向量回归的短期负荷预测

李笋1,王超2,张桂林3*,徐志根2,程涛2,王义元2,王瑞琪1   

  1. 1. 国网山东省电力公司, 山东 济南 250001; 2. 国网山东省电力公司青岛供电公司, 山东 青岛 266002;3. 山东科技大学电气与自动化工程学院, 山东 青岛 266590
  • 收稿日期:2017-08-03 出版日期:2017-12-20 发布日期:2017-08-03
  • 通讯作者: 张桂林(1983— ),男,山东寿光人,讲师,博士,主要研究方向为电力系统智能控制. E-mail: zhangguilin@sdust.edu.cn E-mail:lisun@163.com
  • 作者简介:李笋(1985— ),男,山东肥城人,高级工程师,工学硕士,主要研究方向为电力系统分析运行和电力系统智能技术. E-mail:lisun@163.com
  • 基金资助:
    国家自然科学基金资助项目(61503216;61603320);山东省自然科学基金资助项目(ZR2017BEE058)

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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摘要： 对短期负荷特性进行分析,选取与负荷相关的气象因素、日期类型、前几日负荷作为最大(最小)负荷预测回归模型的输入。夏冬两季休息日的负荷特性与春秋两季不一致,根据气象因素修正日期类型对应的数值。采用最小二乘支持向量机(least squares support vector machine, LSSVM)建立气象因素和日期类型与最大(最小)负荷的映射关系。利用相似日法计算日负荷变化系数,在预测最大负荷和最小负荷基础上,计算预测日各点负荷。算例分析验证了本研究预测模型的有效性。

关键词: 支持向量回归, 最大负荷, 相似日, 负荷预测

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

中图分类号: 

  • TM715
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