为提高超短期风功率的预测精度,提出一种改进的基于变分模态分解的卷积神经网络（AVMD-CNN）、门控循环单元（GRU）和注意力机制（Attention）的超短期风功率预测模型。首先利用改进的VMD将风功率序列分解为K个子模态;然后将各子模态利用样本熵（SE）和中心频率进行分类,根据分类结果对各子模态分别给定归一化方式,并按SE值分别输入到GRU-Attention和CNN-GRU-Attention模型中进行训练和预测;最后将各子模态预测结果叠加得到最终结果,从而完成超短期风功率预测。以决定系数（R2）、平均绝对误差（MAE）、均方根误差（RMSE）以及平均绝对百分比误差（MAPE）为精度评估指标,实际算例表明,所提出模型的R2较文中其他方法平均提高12.06%,MAE、RMSE以及MAPE分别平均降低59.36%、62.49%和48.34%,具有较高的预测精度。
Abstract
In order to improve the forecast accuracy of ultra-short-term wind power, an improved ultra-short-term wind power forecast model based on variational mode decomposition convolutional neural network （AVMD-CNN）, gated recurrent unit （GRU） and attention mechanism （Attention） is proposed. Firstly, the wind power sequence is decomposed into K sub-modes by using the improved VMD. Then, each sub-mode is classified by sample entropy （SE） and center frequency. According to the classification results, each sub-mode is given a normalization method, and input into GRU-Attention and CNN-GRU-Attention models for training and forecasting according to SE values. Finally, the final results are obtained by superimposing the forecast results of each sub-mode, so as to complete the ultra-short-term wind power forecast. Using the determination coefficient（R2）, mean absolute error （MAE）, root mean square error （RMSE）, and mean absolute percentage error （MAPE） as the accuracy assessment indexes, the actual arithmetic examples show that the R2 of the proposed model is improved by 12.06% on average compared with other methods, and the MAE, RMSE, and MAPE are reduced by 59.36%, 62.49%, and 48.34% respectively, with high prediction accuracy.
关键词
风功率 /
预测 /
变分模态分解 /
卷积神经网络 /
注意力机制 /
样本熵
Key words
wind power /
forecasting /
variational mode decomposition /
convolutional neural network /
attention mechanism /
sample entropy
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 赵泽妮, 云斯宁, 贾凌云, 等. 基于统计模型的短期风能预测方法研究进展[J]. 太阳能学报, 2022, 43(11): 224-234.
ZHAO Z N, YUN S N, JIA L Y, et al.Recent progress in short-term forecasting of wind energy based on statistical models[J]. Acta energiae solaris sinica, 2022, 43(11): 224-234.
[2] 贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.
JIA L Y, YUN S N, ZHAO Z N, et al.Recent progress of short-term forecasting of photovoltaic generation based on artificial neural networks[J]. Acta energiae solaris sinica, 2022, 43(12): 88-97.
[3] 向玲, 刘佳宁, 苏浩, 等. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报, 2022, 43(8): 334-339.
XIANG L, LIU J N, SU H, et al.Research on multi-step wind speed forecast based on CEEMDAN secondary decomposition and LSTM[J]. Acta energiae solaris sinica, 2022, 43(8): 334-339.
[4] WANG L N, MAO M X, XIE J L, et al.Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model[J]. Energy, 2023, 262: 125592.
[5] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[6] LALA H, KARMAKAR S.Detection and experimental validation of high impedance arc fault in distribution system using empirical mode decomposition[J]. IEEE systems journal, 2020, 14(3): 3494-3505.
[7] 杨海柱, 田馥铭, 张鹏, 等. 基于CEEMD-FE和AOA-LSSVM的短期电力负荷预测[J]. 电力系统保护与控制, 2022, 50(13): 126-133.
YANG H Z, TIAN F M, ZHANG P, et al.Short-term load forecasting based on CEEMD-FE-AOA-LSSVM[J]. Power system protection and control, 2022, 50(13): 126-133.
[8] 赵凌云, 刘友波, 沈晓东, 等. 基于CEEMDAN和改进时间卷积网络的短期风电功率预测模型[J]. 电力系统保护与控制, 2022, 50(1): 42-50.
ZHAO L Y, LIU Y B, SHEN X D, et al.Short-term wind power prediction model based on CEEMDAN and an improved time convolutional network[J]. Power system protection and control, 2022, 50(1): 42-50.
[9] 张婉莹, 何耀耀, 杨善林. 基于TVFEMD-SE和YJQRG的短期风电功率多步概率密度预测[J]. 系统工程理论与实践, 2022, 42(8): 2225-2242.
ZHANG W Y, HE Y Y, YANG S L.Multi-step probability density prediction of short-term wind power based on TVFEMD-SE and YJQRG[J]. Systems engineering-theory & practice, 2022, 42(8): 2225-2242.
[10] WANG F, XUAN Z M, ZHEN Z, et al.A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework[J]. Energy conversion and management, 2020, 212: 112766.
[11] WANG H Z, LEI Z X, ZHANG X, et al.A review of deep learning for renewable energy forecasting[J]. Energy conversion and management, 2019, 198: 111799.
[12] LEE D H, KIM K.PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information[J]. Renewable energy, 2021, 173: 1098-1110.
[13] 盛四清, 金航, 刘长荣. 基于VMD-WSGRU的风电场发电功率中短期及短期预测[J]. 电网技术, 2022, 46(3): 897-904.
SHENG S Q, JIN H, LIU C R.Short-term and mid-short-term wind power forecasting based on VMD-WSGRU[J]. Power system technology, 2022, 46(3): 897-904.
[14] 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12): 4370-4376.
ZHAO B, WANG Z P, JI W J, et al.A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power system technology, 2019, 43(12): 4370-4376.
[15] STRATIGAKOS A, BACHOUMIS A, VITA V, et al.Short-term net load forecasting with singular spectrum analysis and LSTM neural networks[J]. Energies, 2021, 14(14): 4107.
[16] 杨国清, 刘世林, 王德意, 等. 基于Attention-GRU风速修正和Stacking的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 273-281.
YANG G Q, LIU S L, WANG D Y, et al.Short-term wind power forecasting based on Attention-GRU wind speed correction and stacking[J]. Acta energiae solaris sinica, 2022, 43(12): 273-281.
[17] XIE Y T, SUN W, REN M M, et al.Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs[J]. Expert systems with applications, 2023, 217: 119469.
[18] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.
[19] 郅伦海, 訾勇, 徐凯. 基于变分模态分解和神经网络的风速组合预测[J]. 合肥工业大学学报(自然科学版), 2022, 45(11): 1505-1510, 1584.
ZHI L H, ZI Y, XU K.Combination prediction of wind speed based on variational mode decomposition and neural network[J]. Journal of Hefei University of Technology(natural science), 2022, 45(11): 1505-1510, 1584.
[20] 刘栋, 魏霞, 王维庆, 等. 基于VMD-WPE和SSA-ELM的短期风电功率预测研究[J]. 太阳能学报, 2022, 43(12): 360-367.
LIU D, WEI X, WANG W Q, et al.Short term wind power forecasting based on VMD-WPE and SSA-ELM[J]. Acta energiae solaris sinica, 2022, 43(12): 360-367.
基金
国家自然科学基金（51867006）; 贵州省科技厅（黔科合支撑[2021]一般442、[2022]一般264、[2023]一般096、[2023]一般179）
PDF(2635 KB)
