Short-term wind speed forecasting based on spatial-temporal graph transformer networks
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Title
Short-term wind speed forecasting based on spatial-temporal graph transformer networks
Authors
Keywords
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Journal
ENERGY
Volume 253, Issue -, Pages 124095
Publisher
Elsevier BV
Online
2022-04-28
DOI
10.1016/j.energy.2022.124095
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- Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction
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- (2018) Jingjing Song et al. APPLIED ENERGY
- Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach
- (2018) Qiaomu Zhu et al. Energies
- Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
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- (2018) Hui Liu et al. ENERGY CONVERSION AND MANAGEMENT
- Short-term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
- (2018) Xiong Luo et al. IEEE Transactions on Industrial Informatics
- Spatio-temporal Graph Deep Neural Network for Short-term Wind Speed Forecasting
- (2018) Mahdi Khodayar et al. IEEE Transactions on Sustainable Energy
- Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting
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- Deep belief network based deterministic and probabilistic wind speed forecasting approach
- (2016) H.Z. Wang et al. APPLIED ENERGY
- Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA
- (2015) Osamah Basheer Shukur et al. RENEWABLE ENERGY
- Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach
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- Forecasting wind speed with recurrent neural networks
- (2012) Qing Cao et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model
- (2010) Erasmo Cadenas et al. RENEWABLE ENERGY
- Day-ahead wind speed forecasting using f-ARIMA models
- (2008) Rajesh G. Kavasseri et al. RENEWABLE ENERGY
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