A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage-Reference-Cited by-同舟云学术

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A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage

Published:2024-02-28 Issue:1 Volume:46 Page:3338-3362
ISSN:1556-7036
Container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
language:en
Short-container-title:Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Author:

Zhou Pinhan¹^ORCID,Shen Lian²,Han Yan¹,Mi Lihua¹,Xu Guoji³

Affiliation:

1. Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology, Changsha, China

2. School of Civil Engineering, Changsha University, Changsha, China

3. School of Civil Engineering, Southwest Jiaotong University, Chengdu, China

Funder

National Natural Science Foundation of China

the Science Research Foundation of Hunan Provincial Department of Education

Hunan Provincial Talent Recruitment Project Fund

the Practical Innovation and the Entrepreneurship Ability Improvement Plan of Changsha University of Science and Technology

Training Program for Excellent Young Innovators of Changsha

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567036.2024.2318485

Reference38 articles.

1. Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

2. Neural machine translation by jointly learning to align and translate;Bahdanau D.;arXiv preprint arXiv:1409.0473,2014

3. Locally recurrent neural networks for wind speed prediction using spatial correlation

4. Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output

5. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cited by 1 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. Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S;Energy;2024-10

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