Affiliations: [a] School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
| [b] Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
Correspondence:
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Corresponding author. Jinxing Che, School of Science, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China. E-mail: [email protected].
Abstract: Wind energy needs to be used efficiently, which depends heavily on the accuracy and reliability of wind speed forecasting. However, the volatility and nonlinearity of wind speed make this difficult. In volatility and nonlinearity reduction, we sequentially apply complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to secondarily decompose the wind speed data. This framework, however, requires effectively modeling multiple uncertainty components. Eliminating this limitation, we integrate crow search algorithm (CSA) with deep belief network (DBN) to generate a unified optimal deep learning system, which not only eliminates the influence of multiple uncertainties, but also only adopts DBN as a predictor to realize parsimonious ensemble. Two experiments demonstrate the superiority of this system.
