Short-term wind power forecasting based on Attention-GRU wind speed correction and stacking

被引：0

作者：

Yang G. ^{[1
,2
]}

Liu S. ^{[1
]}

Wang D. ^{[1
,2
]}

Wang W. ^{[1
]}

Liu J. ^{[1
,2
]}

机构：

[1] School of Electrical Engineering, Xi'an University of Technology, Xi'an

[2] Xi'an Key Laboratory of Smart Energy, Xi'an University of Technology, Xi'an

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 12期

关键词：

Deep learning; Ensemble learning; Forecasting; Weather forecasting; Wind power;

D O I：

10.19912/j.0254-0096.tynxb.2021-0712

中图分类号：

学科分类号：

摘要：

In view of the low accuracy of wind power forecasting due to the deviation of NWP wind speed and actual wind speed when using numerical weather prediction(NWP) data, this paper proposes a short-term wind power forecasting model based on Attention-GRU NWP wind speed correction and multi-algorithm fusion under Stacking framework. Firstly, the Pearson correlation coefficient between NWP prediction wind speed and actual wind speed is analyzed, and a wind speed correction model based on Attention-GRU is established to improve the precision of prediction wind speed. Secondly, considering the meteorological factors such as wind direction, temperature, humidity, air pressure and air density, a multi-algorithm wind power forecasting model based on Stacking framework is proposed, which integrates XGBoost, LSTM, SVR and LASSO. Grid search and cross-validation are used to optimize the model parameters. Finally, two typical wind farms in Northwest and Northeast China are selected to verify the model. The results show that the proposed model can improve the precision of NWP wind speed forecast and effectively improve the forecasting effect of wind power. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：273 / 281

页数：8

共 21 条

[1]

LIN B Q., China's electric power development achievements during the "13th Five-Year Plan"period and prospects for the "14th Five-Year Plan, China electric power, 12, pp. 22-23, (2020)

[2]

YANG X Y, XIAO Y, CHEN S Y., Wind speed and generated power forecasting in wind farm, Proceedings of the CSEE, 11, pp. 3-7, (2005)

[3]

XUE Y S, YU C, ZHAO J H, Et al., Comments on short-term and ultra-short-term wind power forecasting, Automation of electric power systems, 39, 6, pp. 141-151, (2015)

[4]

LIU S, ZHU Y L, ZHANG K, Et al., Short-term wind power forecasting based on error correction ARMA-GARCH model, Acta energiae solaris sinica, 41, 10, pp. 268-275, (2020)

[5]

YANG L, HE M, ZHANG J, Et al., Support-vector-machine-enhanced Markov model for short-term wind power forecast, IEEE transactions on sustainable energy, 6, 3, pp. 791-799, (2017)

[6]

HUANG H, JIA R, DONG K S., Ultra short-termcombined forecasting of wind power with NAR neural network based on spatio-temporal correlation, Acta energiae solaris sinica, 41, 10, pp. 311-316, (2020)

[7]

HAN L, ZHANG R, WANG X, Et al., Multi-step wind power forecast based on VMD-LSTM, IET renewable power generation, 13, 10, pp. 1690-1700, (2019)

[8]

WANG X, CAI X, LI Z., Ultra-short-term wind power forecasting method based on a cross LOF preprocessing algorithm and an attention mechanism, Power system protection and control, 48, 23, pp. 92-99, (2020)

[9]

DU P W., Ensemble machine learning-based wind forecasting to combine NWP output with data from weather station, IEEE transactions on sustainable energy, 10, 4, pp. 2133-2141, (2019)

[10]

ANDRADE J R., BESSA R J., Improving renewable energy forecasting with a grid of numerical weather predictions, IEEE transactions on sustainable energy, 8, 4, pp. 1571-1580, (2017)

← 1 2 3 →