Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode d...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 286; p. 129640
Main Authors: Hou, Guolian, Wang, Junjie, Fan, Yuzhen
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2024
Subjects:
algorithms
arithmetics
Bidirectional long short-term memory
China
energy
Enhanced arithmetic optimization algorithm
Error correction
Kernel principal component analysis
Multistep wind power prediction
neural networks
prediction
principal component analysis
Secondary decomposition
wind farms
wind power
China
Multistep wind power prediction
Error correction
Enhanced arithmetic optimization algorithm
Bidirectional long short-term memory
Kernel principal component analysis
Secondary decomposition
ISSN:0360-5442
Online Access:Get full text
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Summary:Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), the kernel principal component analysis (KPCA), an enhanced arithmetic optimization algorithm (ENAOA), a bidirectional long short-term memory (BILSTM) neural network, and error correction, was designed for short-term wind power forecasting. First, the collected original wind power data were decomposed into multiple intrinsic mode functions (IMFs) through a secondary decomposition composed of the CEEMDAN and VMD, which eliminated the interactions between different components to achieve denoising. Second, the KPCA was adopted to reduce the dimensionality of the multiple IMFs by extracting the principal components, effectively reducing the complexity of the multidimensional IMF data and improving the forecasting efficiency of the proposed prediction model. Subsequently, an ENAOA was proposed based on the Sobol sequence, adaptive T-distribution, and random walk strategy to optimize the BILSTM parameters. Finally, multiple preprocessed components were predicted by the optimized BILSTM, after which error correction was performed to obtain the final prediction results, which could further reduce the forecast error of the designed prediction model. Based on two sets of data collected from a wind farm in northwest China, the simulation results of 1-step, 4-step, 7-step, and 10-step forecasting revealed that compared with other incomplete models, the various algorithms adopted in the hybrid forecasting model reduced the prediction errors to different degrees, significantly enhanced the wind power prediction performance, and validated the effectiveness and feasibility of the proposed model. •Original wind power data is preprocessed through CEEMNAN and VMD.•KPCA is adopted to extract principal components of several IMFs.•Propose an improved ENAOA optimization algorithm through three improvements.•Error correction is employed to further increase the forecast accuracy.•The hybrid multi-step forecasting model greatly reduces the prediction error.
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ISSN:0360-5442
DOI:10.1016/j.energy.2023.129640