A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed-Reference-Cited by-同舟云学术

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

Published:2020-02 Issue:2 Volume:23 Page:177-206
ISSN:1095-4244
Container-title:Wind Energy
language:en
Short-container-title:Wind Energy

Author:

Tian Zhongda¹^ORCID,Li Shujiang¹,Wang Yanhong¹

Affiliation:

1. College of Information Science and EngineeringShenyang University of Technology Shenyang 110870 China

Funder

Natural Science Foundation of Liaoning Province of China

the Science Research Project of Liaoning Education Department

Publisher

Wiley

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/we.2422

Reference52 articles.

1. Optimizing wind farm siting to reduce power system impacts of wind variability

2. Current status and future prospects of continuously variable speed wind turbines: A systematic review

3. Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

4. An improved random forest model of short-term wind-power forecasting to enhance accuracy, efficiency, and robustness

5. An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach

Cited by 92 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. Experimental study on the periodicity of wake flow of a vertical staggered wind turbine fleet;Ocean Engineering;2024-10

2. Short-term wind power forecasting using the hybrid model of multivariate variational mode decomposition (MVMD) and long short-term memory (LSTM) neural networks;Electrical Engineering;2024-08-26

3. Short-term wind power prediction based on ICEEMDAN-Correlation reconstruction and BWO-BiLSTM;Electrical Engineering;2024-07-11

4. Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast;Çukurova Üniversitesi Mühendislik Fakültesi Dergisi;2024-07-11

5. Performance enhancement of short-term wind speed forecasting model using Realtime data;PLOS ONE;2024-05-31