Short‐Term Wind Speed and Direction Forecasting by <scp>3DCNN</scp> and Deep Convolutional <scp>LSTM</scp>-Reference-Cited by-同舟云学术

Short‐Term Wind Speed and Direction Forecasting by 3DCNN and Deep Convolutional LSTM

Published:2022-07-08 Issue:11 Volume:17 Page:1620-1628
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Transactions Elec Engng

Author:

Sari Anggraini Puspita¹²,Suzuki Hiroshi¹,Kitajima Takahiro¹,Yasuno Takashi¹,Prasetya Dwi Arman²,Arifuddin Rahman³

Affiliation:

1. Department of Electrical and Electronic Engineering Tokushima University 2–1 Minami Josanjima Tokushima 770–8506 Japan

2. Department of Informatics Universitas Pembangunan Nasional “Veteran” Jawa Timur Jl. Raya Rungkut Madya Gunung Anyar Surabaya 60–294 Indonesia

3. Department of Electrical Engineering University of Merdeka Malang Jl Taman Agung 1 Malang 65–146 Indonesia

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23669

Reference17 articles.

1. IRENA:Global energy transformation a roadmap to 2050 www.irena.org (2019).

2. Prediction model of wind speed and direction using deep neural network;Sari AP;Journal of Electrical Engineering, mechatronic and Computer Science (JEEMECS),2020

3. Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection

4. Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

5. J.Cao D.J.Farnham andU.Lall:Spatial‐temporal wind field prediction by artificial neural networks arXivarXiv:1712.05293 (2017).

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