基于VMD-GWO-ELMAN的光伏功率短期预测方法

doi:10.11930/j.issn.1004-9649.202104033

中国电力 ›› 2022, Vol. 55 ›› Issue (5): 57-65.DOI: 10.11930/j.issn.1004-9649.202104033

基于VMD-GWO-ELMAN的光伏功率短期预测方法

张娜¹, 任强¹, 刘广忱², 郭力萍², 李静宇¹

1. 内蒙古工业大学电力学院，内蒙古呼和浩特 010051;
2. 内蒙古自治区电能变换传输与控制重点实验室，内蒙古呼和浩特 010051

收稿日期:2021-04-25 修回日期:2022-03-07 出版日期:2022-05-28 发布日期:2022-05-18
作者简介:张娜（1981—），女，通信作者，博士，副教授，从事智能配网、新能源出力预测、态势预测研究，E-mail：zhangna2010337@163.com;任强（1996—），男，硕士研究生，从事新能源出力预测研究，E-mail：renqiang6442@163.com
基金资助:
国家自然科学基金资助项目（51867020）；内蒙古自治区自然科学基金资助项目（2020BS05002）；内蒙古科技计划项目（201802030）。

PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN

ZHANG Na¹, REN Qiang¹, LIU Guangchen², GUO Liping², LI Jingyu¹

1. College of Electricity, Inner Mongolia University of Technology, Hohhot 010051, China;
2. Inner Mongolia Key Laboratory of Electrical Power Conversion, Transmission and Control, Hohhot 010051, China

Received:2021-04-25 Revised:2022-03-07 Online:2022-05-28 Published:2022-05-18
Supported by:
This work is supported by National Natural Science Foundation of China（No.51867020）, Natural Science Foundation of Inner Mongolia Autonomous Region (No.2020BS05002), Inner Mongolia Science and Technology Project (No.201802030).

摘要/Abstract

摘要： 以进一步提高光伏输出功率短期预测的准确性和可靠性为目标，针对传统Elman神经网络权值和阈值盲目随机的缺点以及光伏输出功率信号波动性和非平稳性的特点，提出一种基于变分模态分解（VMD）和灰狼优化算法（GWO）优化Elman神经网络的光伏输出功率短期预测模型。首先，使用K-means算法对原始数据按天气类型进行聚类；然后，使用VMD对每一类型天气光伏输出功率数据进行分解，分别将各分解子序列输入经GWO优化的Elman神经网络进行光伏输出功率预测；最后，将各预测结果进行叠加。实例证明：该模型的预测精度有所提升。

关键词: K-means聚类, 变分模态分解, 灰狼优化算法, Elman神经网络, 短期光伏功率预测

Abstract: This paper aims to further improve the accuracy and reliability of short-term photovoltaic (PV) output power forecasting. Considering the blindness and randomness of weights and thresholds of traditional Elman neural networks and the fluctuation and nonstationarity of PV output power signal, the paper proposes a short-term prediction model of PV output power based on variational mode decomposition (VMD) and an Elman neural network optimized by grey wolf optimization (GWO) algorithm. Firstly, the K-means algorithm is used to cluster the original data according to weather types. Then, VMD is employed to decompose the PV output power data of each weather type, and the decomposition subsequences are input into the Elman neural network optimized by GWO for PV output power forecasting. Finally, the forecasting results are superimposed. An example shows that the model has improved forecasting accuracy.

Key words: K-means cluster, variational mode decomposition, grey wolf optimization algorithm, Elman neural network, short-term PV power forecasting

张娜, 任强, 刘广忱, 郭力萍, 李静宇. 基于VMD-GWO-ELMAN的光伏功率短期预测方法[J]. 中国电力, 2022, 55(5): 57-65.

ZHANG Na, REN Qiang, LIU Guangchen, GUO Liping, LI Jingyu. PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN[J]. Electric Power, 2022, 55(5): 57-65.

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基于VMD-GWO-ELMAN的光伏功率短期预测方法

PV Power Short-Term Forecasting Method Based on VMD-GWO-ELMAN

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基于VMD-GWO-ELMAN的光伏功率短期预测方法

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