Offshore wind power forecasting based on WPD and optimised deep learning methods-Reference-Cited by-同舟云学术

Offshore wind power forecasting based on WPD and optimised deep learning methods

Published:2023-12 Issue: Volume:218 Page:119241
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Hanifi Shahram^ORCID,Zare-Behtash Hossein,Cammarano Andrea^ORCID,Lotfian Saeid^ORCID

Funder

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference39 articles.

1. A critical review of wind power forecasting methods-past;Hanifi;Present Future Energ.,2020

2. Challenge of Net-Zero Carbon Construction and Measurement of Energy Consumption and Carbon Emission Reduction to Climate Change , Economy and Job Growths in Hong Kong and Australia;Kit,2022

3. State-of-the-art one-stop handbook on wind forecasting technologies: an overview of classifications, methodologies, and analysis;Yang;J. Clean. Prod.,2021

4. A novel bidirectional mechanism based on time series model for wind power forecasting;Zhao;Appl. Energy,2016

5. Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network;Yu;J. Wind Eng. Ind. Aerod.,2018

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