Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25208
Title: Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network
Authors: Wu, X
Jiang, S
Lai, CS
Zhao, Z
Lai, LL
Keywords: Short-term wind power prediction;data decomposition;combined deep neural network;improved particle swarm optimization algorithm;optimal parameter
Issue Date: 14-Sep-2022
Publisher: MDPI AG
Citation: Lai CS, et. al. (2022) Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network. Energies. vol.15(18), pp.1-16. https://doi.org/10.3390/en15186734
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.
URI: http://bura.brunel.ac.uk/handle/2438/25208
DOI: http://dx.doi.org/10.3390/en15186734
ISSN: 1996-1073
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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