Wind Speed Forecasting Using an Ensemble Deep Random Vector Functional Link Neural Network Based on Parsimonious Channel Mixing-Reference-Cited by-同舟云学术

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Wind Speed Forecasting Using an Ensemble Deep Random Vector Functional Link Neural Network Based on Parsimonious Channel Mixing

Published:2024-06-30 Issue: Volume:1 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Cheng Ruke¹,Gao Ruobin¹,Hu Minghui²,Suganthan Ponnuthurai Nagaratnam³,Fai Yuen Kum¹

Affiliation:

1. Nanyang Technological University,School of Civil and Environmental Engineering,Singapore

2. Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore

3. Qatar University,KINDI Center for Computing Research, College of Engineering,Doha,Qatar

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650817.pdf?arnumber=10650817

Reference30 articles.

1. Nine novel ensemble models for solar radiation forecasting in Indian cities based on VMD and DWT integration with the machine and deep learning algorithms

2. Gray wolf optimization-based wind power load mid-long term forecasting algorithm

3. A review of wind speed and wind power forecasting with deep neural networks

4. Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network

5. Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships

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