Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network

Xiaomei Wu, Songjun Jiang, Chun Sing Lai (), Zhuoli Zhao and Loi Lei Lai ()
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Xiaomei Wu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Songjun Jiang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chun Sing Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zhuoli Zhao: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Loi Lei Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Energies, 2022, vol. 15, issue 18, 1-16

Abstract: A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.

Keywords: short-term wind power prediction; data decomposition; combined deep neural network; improved particle swarm optimization algorithm; optimal parameter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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