Localized Convolutional Neural Networks for Geospatial Wind Forecasting
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References listed on IDEAS
- Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
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- Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
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- Ming Wei & Xue-yi You, 2022. "Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4003-4018, September.
- Zahra Jahangiri & Mackenzie Judson & Kwang Moo Yi & Madeleine McPherson, 2023. "A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System," Energies, MDPI, vol. 16(3), pages 1-21, January.
- Manaf Ahmed & Véronique Maume‐Deschamps & Pierre Ribereau, 2022. "Recognizing a spatial extreme dependence structure: A deep learning approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
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Keywords
convolutional neural networks; recurrent neural networks; deep learning; machine learning; spatial-temporal wind forecasting;All these keywords.
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