A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition

Hao Yin, Zuhong Ou, Shengquan Huang and Anbo Meng

Energy, 2019, vol. 189, issue C

Abstract: Wind power forecasting is crucial for the economic dispatch and operation of power system. In this study, a novel hybrid wind power prediction approach is proposed by applying a cascaded deep learning model to extract the implicit meteorological and temporal characteristics of each subseries generated by a two-layer of mode decomposition method. In the proposed model, the empirical mode decomposition is employed to decompose the original time series into a set of intrinsic mode functions (IMFs) and the variational mode decomposition is applied to further decompose the IMF1 sub-layers into several sub-series because of the irregular feature of IMF1. To make use of the coupling relationship between wind power sub-layer, wind speed sub-layer and wind direction, convolutional neural network is used to extract the implicit features of these relationship and then long short-term memory utilizes these features as inputs and further extract the temporal correlation hidden features in each time sub-series. The eventual predicted results are obtained by superimposing the predicted values of all subsequences. The experimental results illustrate that: (a) The prediction performance is obviously improved when the proposed two-layer of decomposition is considered. (b) To achieve better prediction accuracy, it is proven to be an effective way to apply convolutional neural network and long short-term memory to extract the implicit meteorological relationship and the temporal correlation characteristic hidden in each decomposed time sub-series, respectively. (c) The proposed hybrid model outperforms other hybrid models involved in this study and shows a promising prospect in the short-term wind power prediction.

Keywords: Wind power prediction; Empirical mode decomposition; Variational mode decomposition; Convolutional neural network; Long short-term memory (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:189:y:2019:i:c:s0360544219320110

DOI: 10.1016/j.energy.2019.116316

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