2-D regional short-term wind speed forecast based on CNN-LSTM deep learning model

2021 ◽  
Vol 244 ◽  
pp. 114451
Author(s):  
Yaoran Chen ◽  
Yan Wang ◽  
Zhikun Dong ◽  
Jie Su ◽  
Zhaolong Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Learning Model ◽  
Short Term ◽  
Deep Learning Model ◽  
Wind Speed Forecast

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model

2020 ◽  
Vol 260 ◽  
pp. 114259 ◽  
Author(s):  
Yongqi Liu ◽  
Hui Qin ◽  
Zhendong Zhang ◽  
Shaoqian Pei ◽  
Zhiqiang Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Learning Model ◽  
Variational Bayesian ◽  
Wind Speed Forecasting ◽  
Deep Learning Model

Short-term prediction of lane-level traffic speeds: A fusion deep learning model

2019 ◽  
Vol 106 ◽  
pp. 1-16 ◽  
Author(s):  
Yuanli Gu ◽  
Wenqi Lu ◽  
Lingqiao Qin ◽  
Meng Li ◽  
Zhuangzhuang Shao
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Deep Learning Model
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