Wind Power Prediction Based on Recurrent Neural Network with Long Short-Term Memory Units

2018 ◽  
Author(s):  
Danting Dong ◽  
Zhihao Sheng ◽  
Tiancheng Yang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory

A novel wavenets long short term memory paradigm for wind power prediction

2020 ◽  
Vol 269 ◽  
pp. 115098 ◽  
Author(s):  
Farah Shahid ◽  
Aneela Zameer ◽  
Ammara Mehmood ◽  
Muhammad Asif Zahoor Raja
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory

scholarly journals Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5400
Author(s):  
Pei Zhang ◽  
Chunping Li ◽  
Chunhua Peng ◽  
Jiangang Tian
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory ◽  
The Impact

To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

A long short-term memory based wind power prediction method

2021 ◽  
Author(s):  
Yufeng Huang ◽  
Min Ding ◽  
Zhijian Fang ◽  
Qingyi Wang ◽  
Zhili Tan ◽  
...  
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Prediction Method ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory
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