A hybrid wind speed prediction model using improved CEEMDAN and Autoformer model with auto-correlation mechanism-Reference-Cited by-同舟云学术

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A hybrid wind speed prediction model using improved CEEMDAN and Autoformer model with auto-correlation mechanism

Published:2024-04 Issue: Volume:64 Page:103687
ISSN:2213-1388
Container-title:Sustainable Energy Technologies and Assessments
language:en
Short-container-title:Sustainable Energy Technologies and Assessments

Author:

Bommidi Bala Saibabu,Teeparthi Kiran^ORCID

Publisher

Elsevier BV

Reference37 articles.

1. The impact of financial development and economic growth on renewable energy consumption: Empirical analysis of India;Eren;Sci Total Environ,2019

2. Wind energy and CO 2 emissions: AMG estimations for selected countries;Güney;Environ Sci Pollut Res,2022

3. A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network;Sibtain;Energy Convers Manage,2022

4. Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine;Dokur;Energy,2022

5. Overview of wind power intermittency impacts on power systems;Albadi;Electr Power Syst Res,2010

Cited by 2 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. Short-term wind power prediction method based on multivariate signal decomposition and RIME optimization algorithm;Expert Systems with Applications;2025-01

2. Short-term wind speed prediction based on time series trends and periodic characteristics;Journal of Physics: Conference Series;2024-08-01

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