An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition

Published:2022-05-16 Issue:8 Volume:10 Page:3001-3022
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Science & Engineering

Author:

Tuerxun Wumaier¹²^ORCID,Xu Chang³^ORCID,Guo Hongyu³,Guo Lei¹⁴^ORCID,Zeng Namei⁵,Cheng Zhiming³

Affiliation:

1. College of Water Conservancy and Hydro‐Power Engineering of HoHai University Nanjing China

2. College of Hydraulic and Civil Engineering of Xinjiang Agricultural University Urumqi China

3. College of Energy and Electrical Engineering of HoHai University Nanjing China

4. College of Mechanical Engineering of Nanchang Institute of Technology NanChang China

5. Clean Energy Branch of Huaneng International Power Jiangsu Energy Development Co. Nanjing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1183

Reference30 articles.

1. Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

2. A combination forecasting model of wind speed based on decomposition

3. A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

4. A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China

5. A new hybrid framework for probabilistic wind speed prediction using deep feature selection and multi-error modification

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Risk assessment study of hydrogen energy storage system based on KPCA-TSO-LSSVM;International Journal of Hydrogen Energy;2024-08

2. Cosine-enhanced tuna swarm optimized exponential entropy segmentation method for sand grain microscopic images;Computers & Geosciences;2024-08

3. Multi-Site Wind Speed Prediction Based on Graph Embedding and Cyclic Graph Isomorphism Network (GIN-GRU);Energies;2024-07-17

4. Interval prediction of short‐term photovoltaic power based on an improved GRU model;Energy Science & Engineering;2024-07

5. Implementation of Deep Generative Model for Generating Synthetic Wind Speed Data for Offshore Wind Turbine Maintenance Exploration;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3