Forecasting Model of Wind Speed and Direction by Convolutional Neural Network - Deep Convolutional Long Short Term Memory

Author:

Sari Anggraini Puspita1,Prasetya Dwi Arman2,Yasuno Takashi3,Sihananto Andreas Nugroho1,Al Haromainy Muhammad Muharrom1,Saputra Wahyu S.J.1

Affiliation:

1. Universitas Pembangunan Nasional “Veteran” Jawa Timur,Informatics Dept.,Surabaya,Indonesia

2. Universitas Pembangunan Nasional “Veteran” Jawa Timur,Data Science Dept.,Surabaya,Indonesia

3. Tokushima University,Electrical and Electronic Engineering Dept.,Tokushima,Japan

Publisher

IEEE

Reference18 articles.

1. Learning to diagnose with LSTM recurrent neural networks;lipton,2017

2. Regulazing RNNs for caption generation by reconstructing the past with the present;chen;IEEE explore CVPR paper provided Computer Vision Foundation,2018

3. Convolutional LSTM network: A machine learning approach for precipitation nowcasting;shi,2015

4. Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

5. Prediction of rapid wind speed fluctuation by LSTM using observed data of multiple points;fukuoka;Proc 2019 RISP International Workshop on Nonlinear Circuits Communications and Signal Processing (NCSP2019),2019

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