Deep convolutional long short-term memory for forecasting wind speed and direction-Reference-Cited by-同舟云学术

Deep convolutional long short-term memory for forecasting wind speed and direction

Published:2021-03-19 Issue:2 Volume:14 Page:30-38
ISSN:1882-4889
Container-title:SICE Journal of Control, Measurement, and System Integration
language:en
Short-container-title:SICE Journal of Control, Measurement, and System Integration

Author:

Puspita Sari Anggraini¹²,Suzuki Hiroshi¹,Kitajima Takahiro¹,Yasuno Takashi¹,Arman Prasetya Dwi²,Rabi' Abd.²

Affiliation:

1. Department of Electrical and Electronic Engineering, Tokushima University, Tokushima, Japan

2. Department of Electrical Engineering, University of Merdeka Malang, Malang, Indonesia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/18824889.2021.1894878

Reference19 articles.

1. Renewables 2019. Global status report. [cited 2020 Oct 11]. Available from: https://www.ren21.net/reports/global-status-report/.

2. Short‐term wind speed forecasting using S‐transform with compactly supported kernel

3. Sari AP, Suzuki H, Kitajima T, et al. Prediction of wind speed and direction using encoding-forecasting network with convolutional long short-term memory. In: Proceedings of the 2020 59th Annual Conference of the Society of Instrument and Control Engineers (SICE); Chiang Mai, Thailand: 2020. p. 958–963.

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1. Forecasting System of Wind Speed and Direction by Neural Network;2023 IEEE 9th Information Technology International Seminar (ITIS);2023-10-18

2. Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting;Energies;2023-02-21

3. Forecasting Model of Wind Speed and Direction by Convolutional Neural Network - Deep Convolutional Long Short Term Memory;2022 IEEE 8th Information Technology International Seminar (ITIS);2022-10-19

4. Short‐Term Wind Speed and Direction Forecasting by 3DCNN and Deep Convolutional LSTM;IEEJ Transactions on Electrical and Electronic Engineering;2022-07-08