电气工程学报 ›› 2016, Vol. 11 ›› Issue (4): 47-54.doi: 10.11985/2016.04.008

• 合肥工业大学电气与自动化工程学院专刊(一) • 上一篇    

基于EEMD和IPSO的SVM短期光伏出力预测

朱梅梅1,苏建徽1,陈智慧2   

  1. 1.合肥工业大学教育部光伏系统工程研究中心 合肥 230009
    2.广东易事特电源股份有限公司 东莞 523808
  • 收稿日期:2016-01-07 出版日期:2016-04-25 发布日期:2020-01-02
  • 作者简介:朱梅梅 女 1992年生,硕士研究生,研究方向为电力系统及其自动化、新能源发电技术。|苏建徽 男 1968年生,教授,博士生导师,主要研究方向为太阳能发电技术和电力变换控制技术。
  • 基金资助:
    广东省引进创新科研团队计划项目(2011N015);校博士专项科研资助基金(JZ2015HGBZ0487);台达环境与教育基金会电力电子科教发展计划重大项目资助(DREM2015002)

A Forecasting Model of Support Vector Machine Based on Ensemble Empirical Mode Decomposition and Improved Particle Swarm Optimization

Zhu Meimei1,Su Jianhui1,Chen Zhihui2   

  1. 1.Research Center for Photovoltaic System Engineering Hefei University of Technology Hefei 230009 China
    2.Guangdong East Power Co., Ltd. Dongguan 523808 China
  • Received:2016-01-07 Online:2016-04-25 Published:2020-01-02

摘要:

针对光伏发电短期预测准确性问题,提出一种基于集合经验模态分解(EEMD)和改进粒子群优化算法(IPSO)的支持向量机(SVM)预测模型。该模型选择与预测日具有相同天气类型的历史光伏小时出力数据及相关气象因素作为输入变量,采用EEMD方法将历史光伏小时出力数据分解为一系列相对比较平稳的分量序列,针对不同特征子序列,建立选用不同核函数的SVM模型分别进行短期预测,并采用IPSO对不同SVM模型的参数进行优化。通过建立不同预测模型进行比较分析,验证了本文提出的组合预测模型具有较高的预测精度,对大规模光伏并网电力系统的决策优化调度具有一定的意义和参考价值。

关键词: 光伏发电短期预测, 集合经验模态分解, 改进粒子群优化算法, 支持向量机

Abstract:

A forecasting model of support vector machine (SVM) based on ensemble empirical mode decomposition (EEMD) and improved particle swarm optimization (IPSO) is proposed to tackle with the problem of the accuracy of the short-term forecast of photovoltaic system (PVs) hourly output. Both of historical data for the hourly output of PVs and the related meteorological factors, belong to the days that are similar to the forecast day, are taken into consideration. The historical data for hourly output of PVs is decomposed into a series of relatively stable components by using EEMD method. SVM models with different kernel functions and parameters optimized by IPSO are established to forecast different decomposed components. Then different prediction models are established to compare with each other. And the combined prediction model proposed in this paper is validated to has high prediction accuracy, which has a great significance for economic dispatch of power system incorporating large-scale photovoltaic plant.

Key words: Short-term photovoltaic power output prediction, EEMD, IPSO, SVM

中图分类号: