Wind Speed Prediction Using a SEEMD-LSTM Model-Reference-Cited by-同舟云学术

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Wind Speed Prediction Using a SEEMD-LSTM Model

Published:2023-10-27 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 International Conference on Engineering and Emerging Technologies (ICEET)
language:
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Author:

Bahri Mona¹^ORCID,Vahidnia Sahand²^ORCID,Ghignone Leo³^ORCID,Mohtashamkhani Mahtab⁴^ORCID

Affiliation:

1. The School of Information and Physical Sciences, The University of Newcastle.,Newcastle,Australia

2. Sydney Information Hub University of Sydney,Sydney,Australia

3. Australian Ocean Data Network, Integrated Marine Observing System,Hobart,Australia

4. School of Systems and Computing, University of New South Wales,Canberra,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10525679/10525701/10525919.pdf?arnumber=10525919

Reference15 articles.

1. Day-ahead wind speed forecasting using f-ARIMA models

2. A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

3. Time Series Forecasting Using Smoothing Ensemble Empirical Mode Decomposition and Machine Learning Techniques

4. The accuracy of extrapolation (time series) methods: Results of a forecasting competition

5. Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity

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