基于变分模态分解改进生成对抗网络的短期风电功率预测

doi:10.3969/j.issn.2097-0706.2024.02.004

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (2): 28-35.doi: 10.3969/j.issn.2097-0706.2024.02.004

基于变分模态分解改进生成对抗网络的短期风电功率预测

江善和a(), 李伟b(), 徐小艳a(), 王德凯()

a. 安庆师范大学电子工程与智能制造学院，安徽安庆 246011
b. 安庆师范大学经济与管理学院，安徽安庆 246011

收稿日期:2023-08-30 修回日期:2023-11-03 出版日期:2024-02-25 发布日期:2023-12-19
通讯作者: *王德凯（1997），男，工程师，硕士，从事风电功率预测方面的研究，cm1729@163.com。
作者简介:江善和（1975），男，教授，硕士生导师，博士，从事智能计算与电力系统优化方面的研究，jshxlxlw@163.com；
李伟（1976），女，副教授，硕士，从事区域经济与优化决策方面的研究，feiteng.li@163.com；
徐小艳（1998），女，硕士生，从事风电预测方法方面的研究， 17805620673@163.com。
基金资助:
国家自然科学基金项目(51607004);安徽省自然科学基金项目(2008085MF197)

Short-term wind power forecasting based on variational mode decomposition and generative adversarial networks

JIANG Shanhea(), LI Weib(), XU Xiaoyana(), WANG Dekai()

a. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, China
b. School of Economics and Management, Anqing Normal University, Anqing 246011, China

Received:2023-08-30 Revised:2023-11-03 Online:2024-02-25 Published:2023-12-19
Contact: WANG Dekai
Supported by:
National Natural Science Foundation of China(51607004);Natural Science Foundation of Anhui Province(2008085MF197)

摘要/Abstract

摘要：

风电功率的可预测性和预测准确性取得了一定的研究成果，但风电数据中气象和功率的强非线性制约了短期预测精度的进一步提高，提高短期风电功率预测的精度已成为研究的热点与难点。针对风电数据非线性且非稳定的特点，基于分解思想提出一种基于变分模态分解改进生成对抗网络的短期风电功率预测方法。该方法使用变分模态分解分散风电数据中的非线性，将复杂序列的预测任务转化为多个较为简单序列的预测任务；设计了激活函数和损失函数，解决传统生成对抗网络模型不稳定问题，并对所设计激活函数的关键参数进行了分析。Bengaluru风电场某风机数据的算例测试表明，所提方法取得了较好的预测结果，其均方误差相比长短期记忆网络和变分模态分解-长短记忆网络方法分别下降了79.65%和51.83%。

关键词: 短期风电功率预测, 变分模态分解, 生成对抗网络, 长短期记忆神经网络, 激活函数

Abstract:

Although certain progress has been achieved on wind power predictability and prediction accuracy， the short-term prediction accuracy is restricted by the strong nonlinearity of meteorological and wind power data. Hence， the improvement on short-term wind power prediction is a research hotspot. To deal with the nonlinearity and instability of wind power data， a short-term wind power forecasting method based on variational model decomposition and generative adversarial networks is proposed. The method introduces variational mode decomposition to disperse the nonlinearity of wind power data and decompose the complex sequence of prediction task into several simple sequences. Activation function and loss function are designed to solve the instability of traditional generative adversarial network models， and the key parameters of the designed activation function are analysed. The proposed method is tested on the data from a Bengaluru wind farm， showing a decent prediction result. Its forecasting mean square error is 79.65% and 51.83% lower than that of long short-term memory and variational mode decomposition-long short-term memory，respectively.

Key words: short-term wind power forecasting, variational mode decomposition, generative adversarial network, long short-term memory networks, activation function

中图分类号:

TM761

江善和, 李伟, 徐小艳, 王德凯. 基于变分模态分解改进生成对抗网络的短期风电功率预测[J]. 综合智慧能源, 2024, 46(2): 28-35.

JIANG Shanhe, LI Wei, XU Xiaoyan, WANG Dekai. Short-term wind power forecasting based on variational mode decomposition and generative adversarial networks[J]. Integrated Intelligent Energy, 2024, 46(2): 28-35.

图/表 8

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k	中心频率
2	0.000 40, 0.041 81
3	0.000 40, 0.041 24, 0.083 35
4	0.000 40, 0.041 22, 0.082 79, 0.161 03
5	0.000 39, 0.041 20, 0.081 78, 0.123 27, 0.167 87

l	MAE	MSE	RMSE	δmax
0.01	24.694 4	2 489.314 0	49.893 0	577.994 6
0.05	26.747 6	2 390.992 7	48.897 8	829.124 8
0.10	21.350 2	2 118.766 7	46.030 1	518.077 9
0.20	26.300 1	2 659.534 2	51.570 7	580.099 7
0.30	25.740 3	2 712.210 2	52.078 9	565.314 7
0.40	26.747 7	2 724.080 6	52.192 7	565.858 8
0.50	29.226 6	3 427.549 6	58.545 3	629.489 4

预测模型	MAE	MSE	RMSE	δmax
ARIMA^[27]	53.359 0	17 770.382 1	133.305 6	1 674.928 9
LSTM^[28]	72.260 1	10 412.7773	102.043 0	972.853 4
VMD-ARIMA^[27]	34.521 1	6 054.779 6	77.812 5	1 514.236 6
VMD-LSTM^[28]	39.224 3	4 398.470 7	66.321 0	1 008.752 3
VMD-GAN(l = 0.10)	21.350 2	2 118.766 7	46.030 1	518.077 9

基于变分模态分解改进生成对抗网络的短期风电功率预测

Short-term wind power forecasting based on variational mode decomposition and generative adversarial networks

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