Transfer learning for short-term wind speed prediction with deep neural networks

2016 ◽  
Vol 85 ◽  
pp. 83-95 ◽  
Author(s):  
Qinghua Hu ◽  
Rujia Zhang ◽  
Yucan Zhou
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

2009 ◽  
Vol 72 (4-6) ◽  
pp. 1336-1341 ◽  
Author(s):  
Sancho Salcedo-Sanz ◽  
Ángel M. Pérez-Bellido ◽  
Emilio G. Ortiz-García ◽  
Antonio Portilla-Figueras ◽  
Luis Prieto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Input Data ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Artificial Neural

Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

2009 ◽  
Vol 34 (6) ◽  
pp. 1451-1457 ◽  
Author(s):  
Sancho Salcedo-Sanz ◽  
Ángel M. Pérez-Bellido ◽  
Emilio G. Ortiz-García ◽  
Antonio Portilla-Figueras ◽  
Luis Prieto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Mesoscale Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Fifth Generation ◽  
Speed Prediction ◽  
Artificial Neural

scholarly journals Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4126 ◽  
Author(s):  
Yidi Ren ◽  
Hua Li ◽  
Hsiung-Cheng Lin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Flower Pollination Algorithm ◽  
Short Term ◽  
Neural Network Models ◽  
Wind Speed Prediction ◽  
Flower Pollination ◽  
Speed Prediction

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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