Ultra-short-term Wind Power Prediction Based on Power Correction Under Low Wind Speed and Improved Loss Function

被引：0

作者：

Zang H. ^{[1
]}

Zhao Y. ^{[1
]}

Zhang Y. ^{[1
]}

Cheng L. ^{[1
]}

Wei Z. ^{[1
]}

Qin X. ^{[2
]}

机构：

[1] School of Electrical and Power Engineering, Hohai University, Nanjing

[2] Jiangsu Clean Energy Branch, Huaneng International Power Co., Ltd., Nanjing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 07期

基金：

中国国家自然科学基金;

关键词：

loss function improvement; neural network model; power correction under low wind speed; ultra-short-term; wind power prediction;

D O I：

10.7500/AEPS20230926006

中图分类号：

学科分类号：

摘要：

Wind power has strong fluctuation and randomness. In order to further improve the prediction accuracy of wind power, an ultra-short-term wind power prediction model based on power correction under low wind speed and an improved loss function is proposed. The model uses convolutional neural networks, self-attention mechanisms and bidirectional gated recurrent unit to capture long-term temporal dependencies of wind power sequences. In order to solve the problem that it is difficult for the neural network to accurately fit the waiting wind state under low wind speed, the model corrects the predicted power under low wind speed by predicting wind speed and combining wind power at the current period. To solve the stability problem of parameter training, the model introduces a multivariate nonlinear loss function to extract the correlation between sequences by improving the prediction strategy and shared weights. The results show that the proposed model is superior to the comparison model in many error indices, and can effectively improve the prediction effect of the ultra-short-term wind power. © 2024 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：248 / 257

页数：9

共 25 条

[1] XIAO Bai, ZHANG Bo, WANG Xinwei, Et al., Short-term wind power interval prediction based on combined mode decomposition and deep learning, Automation of Electric Power Systems, 47, 17, pp. 110-117, (2023)
[2] LI Kai, DANG Jie, SUN Xin, Et al., Fast evaluation of transmission reliability margin of power systems considering wind power uncertainty, Power System Technology, 43, 9, pp. 3337-3343, (2019)
[3] DONG Xiaochong, SUN Yingyun, PU Tianjiao, Et al., Ultra-short-term probabilistic forecasting of wind power based on temporal mixture density network, Automation of Electric Power Systems, 46, 14, pp. 93-100, (2022)
[4] SU Xiangjing, NIE Liangzhao, LI Chaojie, Et al., Interpretable power output prediction of multiple wind turbines for offshore wind farm based on multiple spatio-temporal attention graph neural network model, Automation of Electric Power Systems, 47, 9, pp. 88-98, (2023)
[5] CAO Junbo, ZHOU Renjun, DENG Xuehua, Et al., Wind power forecast considering differential times of optimal ARIMA model, Proceedings of the CSU-EPSA, 31, 1, pp. 105-111, (2019)
[6] WANG Yining, XIE Da, WANG Xitian, Et al., Prediction of interaction between grid and wind farms based on PCA-LSTM model, Proceedings of the CSEE, 39, 14, pp. 4070-4081, (2019)
[7] SUN Guoqiang, LIANG Zhi, YU Nayan, Et al., Short-term wind power probability density forecasting based on EWT and quantile regression forest, Electric Power Automation Equipment, 38, 8, pp. 158-165, (2018)
[8] WANG Jinfeng, YANG Yuqi, WEN Dong, Et al., A combined model of chaos prediction of wind power generation time series based on GA-BP and RBF, Power System and Clean Energy, 38, 11, pp. 117-125, (2022)
[9] WU Haotian, SUN Rongfu, LIAO Siyang, Et al., Short-term wind power probability forecasting method based on improved meteorological clustering and classification, Automation of Electric Power Systems, 46, 15, pp. 56-65, (2022)
[10] SHI Jiarong, ZHAO Danmeng, WANG Linhua, Et al., Short-term wind power prediction based on RR-VMD-LSTM, Power System Protection and Control, 49, 21, pp. 63-70, (2021)

← 1 2 3 →