Wind Power Forecasting
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2022 ◽  
Vol 206 ◽  
pp. 107776
Author(s):  
Bangru Xiong ◽  
Lu Lou ◽  
Xinyu Meng ◽  
Xin Wang ◽  
Hui Ma ◽  
...  

2022 ◽  
Author(s):  
J.M. González-Sopeña

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.


2021 ◽  
Author(s):  
Yash Pal ◽  
Kailash Chand Sharma ◽  
Archee Gupta ◽  
Archita Vijayvargia ◽  
Rohit Bhakar

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