Short-term Wind Power Prediction: Feature Selection and ISSA–CNN–BiGRU Approach

您当前的位置：

首页 >

文章列表页 >

INFORMATION ENGINEERING | 更新时间：2024-08-22

- Short-term Wind Power Prediction: Feature Selection and ISSA–CNN–BiGRU Approach
- “最新研究进展：内蒙古风电场采用特征选择和改进麻雀搜索算法优化的CNN-BiGRU模型，实现短期风电功率高精度预测，平均绝对百分比误差仅2.6440%，验证了模型的准确性和泛化能力。”
- Advanced Engineering Sciences Vol. 56, Issue 3, Pages: 228-239(2024)
- 作者机构：
  
  河南理工大学计算机科学与技术学院，河南焦作 454000
- 作者简介：
- 基金信息：
- DOI：10.15961/j.jsuese.202200557
  CLC： TP391.9
- Published：20 May 2024，
  
  Published Online：25 May 2023，
  
  Received：01 June 2022，
  
  Revised：14 October 2022，
- 稿件说明：
扫描看全文
王瑞,徐新超,逯静.基于特征选择及ISSA–CNN–BiGRU的短期风功率预测[J].工程科学与技术,2024,56(3):228–239

Wang Rui,Xu Xinchao,Lu Jing.Short-term wind power prediction: feature selection and ISSA–CNN–BiGRU approach[J].Advanced Engineering Sciences,2024,56(3):228–239
王瑞,徐新超,逯静.基于特征选择及ISSA–CNN–BiGRU的短期风功率预测[J].工程科学与技术,2024,56(3):228–239 DOI： 10.15961/j.jsuese.202200557.

Wang Rui,Xu Xinchao,Lu Jing.Short-term wind power prediction: feature selection and ISSA–CNN–BiGRU approach[J].Advanced Engineering Sciences,2024,56(3):228–239 DOI： 10.15961/j.jsuese.202200557.

Views

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods

Wind Farm Power Transfer Forecasting Method Based on CNN–LSTM

Industrial Control System Intrusion Detection Based on Feature Selection and Temporal Convolutional Network

Deep ADMM Network in Wavelet Domain for Image Restoration

Related Author

LU Jing

XU Xinchao

WANG Rui

Jiajun ZHONG

Shuqi HUANG

Yazhi WANG

Yuandong MO

Wei SUN

Related Institution

School of Computer Science and Technology, Henan Polytechnic University

School of Electromechanical Eng., Lingnan Normal Univ.

School of Eng., The Univ.Edinburgh, Edinburgh

College of Electrical Eng., Sichuan Univ.

School of Control Sci.and Eng., China Univ. of Petroleum (East China)

⁰