A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition. (15th December 2019)

• Record Type: Journal Article
• Authors: Yin, Hao; Ou, Zuhong; Huang, Shengquan; Meng, Anbo
• Is Part Of: Energy. Volume 189(2019)
• Publication Date: 2019-12-15
• Subjects: Wind power prediction -- Empirical mode decomposition -- Variational mode decomposition -- Convolutional neural network -- Long short-term memory; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116316
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: A novel hybrid approach is proposed for short-term wind power forecasting. The secondary mode decomposition is applied to address the IMF1 problem. Convolutional neural network is used to extract the implicit characteristics. Long short-term memory is used to extract the temporal correlation features. The proposed method obtains better performance in prediction accuracy.