|本期目录/Table of Contents|

[1]耿华阳,王 玮.基于数据残余分量与ARMA模型的光伏功率日前预测方法[J].工业仪表与自动化装置,2019,(01):3-7.[doi:1000-0682（2019）01-0000-00]
　GENG Huayang,WANG Wei.Photovoltaic power day-ahead prediction method based on data residual component and ARMA model[J].Industrial Instrumentation & Automation,2019,(01):3-7.[doi:1000-0682（2019）01-0000-00]
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基于数据残余分量与ARMA模型的光伏功率日前预测方法

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2019年01期
页码:
3-7
栏目:
出版日期:
2019-02-15

文章信息/Info

Title:
Photovoltaic power day-ahead prediction method based on data residual component and ARMA model
作者:
耿华阳1; 王 玮1; 2
1.北京交通大学 国家能源主动配电网技术研发中心；
2.北京电动车辆协同创新中心，北京 100044
Author(s):
GENG Huayang1;  WANG Wei1; 2
1. National Active Distribution Network Technology Research Center, Beijing Jiaotong University;
2. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100044, China
关键词:
光伏功率; 日前预测; 残余分量; ARMA模型
Keywords:
photovoltaic power;  day-ahead prediction;  residual component;  ARMA model
分类号:
TM933
DOI:
1000-0682（2019）01-0000-00
文献标志码:
A
摘要:
光伏功率日前预测对电网的规划调度和经济运行管理有着重要作用。根据相似日光伏发电功率曲线，采用日均值法计算得出光伏日内功率曲线的整体变化趋势分量，将原始功率数据曲线与日内整体变化趋势分量求差得到光伏功率的残余分量。基于该残余分量，该文提出了一种改进ARMA模型的日前光伏功率预测方法，该方法通过对残余分量进行预测，将残余分量的预测值与整体变化趋势分量相加得到最终预测结果。通过与传统ARMA模型预测结果进行对比，仿真表明：该方法对不同程度的光伏功率波动情况均有较好的预测精度。
Abstract:
Photovoltaic power day-ahead prediction plays an important role in the planning and dispatching of power grid and the management of economic operation.According to the similar solar power generation power curve,the daily-mean method is used to calculate the overall variation trend curve of the solar energy curve,the residual component is obtained.Based on the residual component,this paper proposes the ARMA model based solar PV power prediction method,which is predicted by the residual component.The prediction value of the residual components and the trend curve are added to the final prediction results. By comparing with the traditional ARMA model, the simulation results show that the proposed method has a better prediction accuracy for different level of PV power fluctuation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2018-09-26
基金项目：国家重点研发计划（2018YFB0905200)
作者简介：耿华阳(1994)，男，黑龙江人，硕士研究生，研究方向为光伏功率预测。
更新日期/Last Update: 2019-01-15