Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

被引：0

作者：

Yang, Xiyun ^{[1
]}

Wei, Peng ^{[1
]}

Liu, Huan ^{[1
]}

Sun, Baojun ^{[2
]}

机构：

[1] North China Elect Power Univ, 2 Beinong Rd, Beijing 102206, Peoples R China

[2] MS Techno, Beijing, Peoples R China

来源：

INDUSTRIAL DESIGN AND MECHANICAL POWER | 2012年 / 224卷

关键词：

wind power plants; wind speed; short-term prediction; power curve; SVM;

D O I：

10.4028/www.scientific.net/AMM.224.401

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

引用

页码：401 / +

页数：2

共 50 条

[21] Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island's Wind Farm [J].

Park, BeomJun ;

Hur, Jin .

ENERGIES, 2017, 10 (06)

[22] Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis [J].

Sun, Zexian ;

Zhao, Mingyu .

IEEE ACCESS, 2020, 8 :134422-134434

[23] Hybrid Ensemble Framework for Short-Term Wind Speed Forecasting [J].

Tang, Zhenhao ;

Zhao, Gengnan ;

Wang, Gong ;

Ouyang, Tinghui .

IEEE ACCESS, 2020, 8 (08) :45271-45291

[24] Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting [J].

Babu, Ramesh N. ;

Arulmozhivarman, P. .

WIND ENGINEERING, 2014, 38 (02) :121-128

[25] Short-Term Wind Speed Forecasting of Knock Airport Based on ANN Algorithms [J].

Yadav, Mukh Raj ;

Singh, Kumar Gaurav ;

Chaturvedi, Anurag .

2017 IEEE INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION, INSTRUMENTATION AND CONTROL (ICICIC), 2017,

[26] SHORT-TERM WIND SPEED FORECASTING BASED ON VARIABLE CORRELATION ATTENTION MECHANISM [J].

Wang X. ;

Zhang K. ;

Li X. ;

Bai K. .

Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2023, 44 (08) :467-476

[27] Multi-Time Series and -Time Scale Modeling for Wind Speed and Wind Power Forecasting Part I: Statistical Methods, Very Short-Term and Short-Term Applications [J].

Colak, Ilhami ;

Sagiroglu, Seref ;

Yesilbudak, Mehmet ;

Kabalci, Ersan ;

Bulbul, H. Ibrahim .

2015 INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS (ICRERA), 2015, :209-214

[28] A review on the forecasting of wind speed and generated power [J].

Ma Lei ;

Luan Shiyan ;

Jiang Chuanwen ;

Liu Hongling ;

Zhang Yan .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2009, 13 (04) :915-920

[29] Short term forecast of wind power generation based on SVM with pattern matching [J].

Zhu, Ziming ;

Zhou, Doudou ;

Fan, Zhong .

2016 IEEE INTERNATIONAL ENERGY CONFERENCE (ENERGYCON), 2016,

[30] Calibration Power Curve of Wind Generator and Forecasting Model of Wind Power Unit [J].

Wei, Chen ;

Yue, Pu .

2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, 2015, :583-587

← 1 2 3 4 5 →