Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys-Reference-Cited by-同舟云学术

Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys

Published:2022-04 Issue: Volume:189 Page:231-244
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Qiao Dalei,Wu Shun^ORCID,Li Ge,You Jiaxing,Zhang Juan,Shen Bilong

Funder

Qinglan Project of Jiangsu Province of China

National Natural Science Foundation of China

Natural Science Research of Jiangsu Higher Education Institutions of China

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference41 articles.

1. A new combined model based on multi-objective salp swarm optimization for wind speed forecasting;Cheng;Appl. Soft Comput. J.,2020

2. Estimating the frequency of exposure to uncertain hazards: impact of wind conditions on concrete dam construction;Chen;J. Construct. Eng. Manag.,2021

3. Wind power prediction in complex terrain using analog ensembles;Birkelund;J. Phys. Conf. Ser.,2018

4. The effect of adverse weather on construction sites;Radevsky;Eff. Advers. Weather Constr. Sites.,2012

5. Failure investigation on a coastal wind farm damaged by super typhoon: a forensic engineering study;Chen;J. Wind Eng. Ind. Aerod.,2015

Cited by 13 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. Study of low-level wind shear at a Qinghai-Tibetan Plateau airport;Atmospheric Research;2024-12

2. Numerical Simulation of Terrain-Adaptive Wind Field Model Under Complex Terrain Conditions;Water;2024-07-28

3. A multi-step wind power group forecasting seq2seq architecture with spatial–temporal feature fusion and numerical weather prediction correction;Energy;2024-03

4. A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis;Engineering Applications of Artificial Intelligence;2024-02

5. Offshore wind power output prediction based on convolutional attention mechanism;Energy Sources, Part A: Recovery, Utilization, and Environmental Effects;2023-10-02