计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 58-65.doi: 10.11896/JsJkx.191000042

• 人工智能 • 上一篇    下一篇

改进的支持向量回归机在电力负荷预测中的应用

唐承娥1, 韦军2   

  1. 1 广西职业技术学院机电与信息工程学院 南宁 530226;
    2 广西壮族自治区招生考试院 南宁 530021
  • 发布日期:2020-07-07
  • 通讯作者: 唐承娥(735438514@qq.com)
  • 基金资助:
    2018年广西高校中青年教师基础能力提升项目(2018KY0956)

Application of Power Load Prediction Based on Improved Support Vector Regression Machine

TANG Cheng-e1 and WEI Jun2   

  1. 1 College of Electromechanical and Information Engineering,Guangxi Vocational and Technical College,Nanning 530226,China
    2 Guangxi Zhuang Autonomous Region Admission Examination Institute,Nanning 530021,China
  • Published:2020-07-07
  • About author:TANG Cheng-e, born in 1983, lecturer.Her main research interests include neural networks and automation of electric power systems.
  • Supported by:
    This work was supported by the 2018 Guangxi College Young and Middle-aged Teacher’s Basic Ability Improvement ProJect(2018KY0956).

摘要: 电力预测是一项重要的工程应用。为了解决多层次粒度支持向量回归机(Dynamical Granular Support Vector Regression Machine,DGSVRM)预测电力负低荷精度的问题,提出一种基于萤火虫群优化(Glowworm Swarm Optimization,GSO)算法与模式搜索算法(Pattern Search,PS)的混合算法来优化DGSVRM预测模型的关键参数。仿真实验表明,通过优化参数之后,预测模型的预测精度得到很大提高。

关键词: 多层次粒度支持向量回归机, 萤火虫群优化, 模式搜索算法

Abstract: Electricity forecasting is an important engineering application.In order to solve the accuracy problem of dynamical granular support vector regression machine for power load forecasting (DGSVRM),this paper proposes a hybrid algorithm of glowworm swarm optimization (GSO) and pattern search (PS) to optimize the key parameters of DGSVRM forecasting model.Simulation results show that the prediction accuracy is greatly improved by optimizing the parameters of the prediction model.

Key words: Dynamical granular support vector regression machine, Glowworm swarm optimization, Pattern search algorithm

中图分类号: 

  • TP391
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