Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression

2021 ◽  
Author(s):  
Jianzhou Wang ◽  
Shuai Wang ◽  
Zhiwu Li
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Quantile Regression ◽  
Swarm Algorithm

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.

Piecewise Aggregate Approximation and Quantile Regression for Wind Speed Analysis

2017 ◽  
Author(s):  
Ronaldo R. B. de Aquino ◽  
Helen Barboza da Silva ◽  
Jonata C. de Albuquerque ◽  
Manuel Herrera ◽  
Aida A. Ferreira ◽  
...  
Keyword(s):  
Wind Speed ◽  
Quantile Regression

A novel loss function of deep learning in wind speed forecasting

Energy ◽  
2021 ◽  
pp. 121808
Author(s):  
Xi Chen ◽  
Ruyi Yu ◽  
Sajid Ullah ◽  
Dianming Wu ◽  
Zhiqiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Loss Function ◽  
Wind Speed Forecasting

GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet

2022 ◽  
Vol 269 ◽  
pp. 112801
Author(s):  
Milad Asgarimehr ◽  
Caroline Arnold ◽  
Tobias Weigel ◽  
Chris Ruf ◽  
Jens Wickert
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Global Ocean ◽  
Gnss Reflectometry ◽  
Learning Development ◽  
Ocean Wind

An Adaptive Hybrid System Using Deep Learning for Wind Speed Forecasting

2021 ◽  
Author(s):  
Paulo S. G. de Mattos Neto ◽  
Joao F. L. de Oliveira ◽  
Domingos S. de O. Santos Junior ◽  
Hugo Valadares Siqueira ◽  
Francisco Madeiro
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Hybrid System ◽  
Wind Speed Forecasting

Forecasting of Wind Speed by Using Deep Learning for Optimal Use of the Energy Produced by Wind Farms

2021 ◽  
pp. 91-99
Author(s):  
Bharat Kumar Saxena ◽  
Sanjeev Mishra ◽  
Komaragiri Venkata Subba Rao
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farms
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