Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model-Reference-Cited by-同舟云学术

Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model

Published:2024 Issue: Volume:12 Page:58750-58777
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Joseph Lionel P.¹^ORCID,Deo Ravinesh C.¹^ORCID,Casillas-Pèrez David²^ORCID,Prasad Ramendra³^ORCID,Raj Nawin¹^ORCID,Salcedo-Sanz Sancho⁴^ORCID

Affiliation:

1. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, Australia

2. Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Madrid, Fuenlabrada, Spain

3. Department of Science, School of Science and Technology, The University of Fiji, Lautoka, Fiji

4. Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain

Funder

University of Southern Queensland (UniSQ) International Ph.D. Stipend and International Ph.D. Tuition Fee Scholarships Awarded through the Graduate Research School (GRS), UniSQ

Spanish Ministry of Science and Innovation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10507841.pdf?arnumber=10507841

Reference102 articles.

1. Transforming Our World: The 2030 Agenda for Sustainable Development

2. Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model

3. Decomposition-based wind speed forecasting model using causal convolutional network and attention mechanism

4. Probabilistic and Deterministic Wind Speed Prediction: Ensemble Statistical Deep Regression Network