Deep convolutional long short-term memory for forecasting wind speed and direction
Author:
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. Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting;Energies;2023-02-21
2. 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
3. Short‐Term Wind Speed and Direction Forecasting by 3DCNN and Deep Convolutional LSTM;IEEJ Transactions on Electrical and Electronic Engineering;2022-07-08
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