Short-term wind power forecasting based on meteorological feature extraction and optimization strategy-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Short-term wind power forecasting based on meteorological feature extraction and optimization strategy

Published:2022-01 Issue: Volume:184 Page:642-661
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Lu Peng,Ye Lin,Pei Ming,Zhao Yongning,Dai Binhua,Li Zhuo

Funder

National Natural Science Foundation of China

Science and Technology Project of State Grid

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference39 articles.

1. Recent advances in electricity price forecasting: a review of probabilistic forecasting;Nowotarski;Renew. Sustain. Energy Rev.,2018

2. Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction;Santhosh;Energy Convers. Manag.,2018

3. Dynamic mean absolute error as new measure for assessing forecasting errors;Frías-Paredes;Energy Convers. Manag.,2018

4. Ultra-short-term probabilistic wind turbine power forecast based on empirical dynamic modeling;Ma;IEEE Trans. Sustain. Energy,2020

5. Two-layer transfer-learning-based architecture for short-term load forecasting;Cai;IEEE Trans. Indus. Inform.,2019

Cited by 49 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. A synchronized multi-step wind speed prediction with adaptive features and parameters selection: Insights from an interaction model;Expert Systems with Applications;2024-12

2. A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection;Expert Systems with Applications;2024-12

3. A review of short-term wind power generation forecasting methods in recent technological trends;Energy Reports;2024-12

4. Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization;Cleaner Energy Systems;2024-12

5. Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm;Cleaner Energy Systems;2024-12

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