Wind Speed Forecasting Model Based on Extreme Learning Machines and Complete Ensemble Empirical Mode Decomposition

2020 5th Asia Conference on Power and Electrical Engineering (ACPEE) ◽

10.1109/acpee48638.2020.9136553 ◽

2020 ◽

Author(s):

Zhou Xing ◽

Yong Zhi ◽

Ru-hai Hao ◽

Hong-wen Yan ◽

Can Qing

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Forecasting Model ◽

Extreme Learning Machines ◽

Wind Speed Forecasting ◽

Learning Machines ◽

Model Based ◽

Mode Decomposition

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Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods

Artificial Intelligence ◽

10.1016/j.artint.2019.103176 ◽

2019 ◽

Vol 277 ◽

pp. 103176 ◽

Author(s):

Hao-Fan Yang ◽

Yi-Ping Phoebe Chen

Keyword(s):

Empirical Mode Decomposition ◽

Representation Learning ◽

Extreme Learning Machines ◽

Wind Speed Forecasting ◽

Learning Machines ◽

Forecasting Methods ◽

Mode Decomposition

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Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems

Journal of Hydrology ◽

10.1016/j.jhydrol.2018.12.060 ◽

2019 ◽

Vol 570 ◽

pp. 167-184 ◽

Author(s):

Xiaohu Wen ◽

Qi Feng ◽

Ravinesh C. Deo ◽

Min Wu ◽

Zhenliang Yin ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Extreme Learning Machines ◽

Two Phase ◽

Learning Machines ◽

Multi Scale ◽

Mode Decomposition ◽

Runoff Prediction ◽

Adaptive Noise ◽

Prediction Problems

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Offshore wind speed forecasting at different heights by using ensemble empirical mode decomposition and deep learning models

Applied Ocean Research ◽

10.1016/j.apor.2021.102937 ◽

2021 ◽

Vol 117 ◽

pp. 102937

Author(s):

Bharat Kumar Saxena ◽

Sanjeev Mishra ◽

Komaragiri Venkata Subba Rao

Keyword(s):

Deep Learning ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Offshore Wind ◽

Learning Models ◽

Wind Speed Forecasting ◽

Mode Decomposition

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Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Renewable Energy ◽

10.1016/j.renene.2016.03.103 ◽

2016 ◽

Vol 94 ◽

pp. 629-636 ◽

Author(s):

Shouxiang Wang ◽

Na Zhang ◽

Lei Wu ◽

Yamin Wang

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Bp Neural Network ◽

Ensemble Empirical Mode Decomposition ◽

Neural Network Method ◽

Wind Speed Forecasting ◽

Mode Decomposition ◽

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Short-term wind speed forecasting approach using Ensemble Empirical Mode Decomposition and Deep Boltzmann Machine

Sustainable Energy Grids and Networks ◽

10.1016/j.segan.2019.100242 ◽

2019 ◽

Vol 19 ◽

pp. 100242 ◽

Author(s):

Madasthu Santhosh ◽

Chintham Venkaiah ◽

D.M. Vinod Kumar

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Boltzmann Machine ◽

Wind Speed Forecasting ◽

Mode Decomposition ◽

Deep Boltzmann Machine

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A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.135934 ◽

2020 ◽

Vol 709 ◽

pp. 135934 ◽

Author(s):

Ekta Sharma ◽

Ravinesh C. Deo ◽

Ramendra Prasad ◽

Alfio V. Parisi

Keyword(s):

Air Quality ◽

Empirical Mode Decomposition ◽

Early Warning ◽

Forecasting Model ◽

Decomposition Algorithms ◽

Extreme Learning Machines ◽

Learning Machines ◽

Mode Decomposition

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A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network

2013 IEEE Power & Energy Society General Meeting ◽

10.1109/pesmg.2013.6672195 ◽

2013 ◽

Author(s):

Yamin Wang ◽

Shouxiang Wang ◽

Na Zhang

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Bp Neural Network ◽

Ensemble Empirical Mode Decomposition ◽

Wind Speed Forecasting ◽

Mode Decomposition ◽

Forecasting Method

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Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks

Energy Conversion and Management ◽

10.1016/j.enconman.2017.10.085 ◽

2018 ◽

Vol 155 ◽

pp. 188-200 ◽

Author(s):

Hui Liu ◽

Xiwei Mi ◽

Yanfei Li

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Empirical Mode Decomposition ◽

Wavelet Packet ◽

Ensemble Empirical Mode Decomposition ◽

Wavelet Packet Decomposition ◽

Wind Speed Forecasting ◽

Mode Decomposition ◽

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A Hybrid Forecasting Model for Wind Energy based on the Complementary Ensemble Empirical Mode Decomposition and Whale Optimized Back Propagation Neural Network

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346709 ◽

2020 ◽

Author(s):

Weihang Li ◽

Runfeng Zhang

Keyword(s):

Neural Network ◽

Wind Energy ◽

Empirical Mode Decomposition ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Ensemble Empirical Mode Decomposition ◽

Forecasting Model ◽

Mode Decomposition ◽

Hybrid Forecasting

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Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction

Energy Conversion and Management ◽

10.1016/j.enconman.2021.113917 ◽

2021 ◽

Vol 233 ◽

pp. 113917

Author(s):

Ming-De Liu ◽

Lin Ding ◽

Yu-Long Bai

Keyword(s):

Neural Networks ◽

Empirical Mode Decomposition ◽

Hybrid Model ◽

Recurrent Neural Networks ◽

Wind Speed Prediction ◽

Model Based ◽

Mode Decomposition ◽

Speed Prediction

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