Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks....

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Bibliographic Details
Main Authors: Weichao Dong, Hexu Sun, Zheng Li, Jingxuan Zhang, Huifang Yang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
K-means clustering
multiscale mathematical morphological decomposition
short-term wind-speed forecasting
stacked denoising autoencoders
Online Access:https://ieeexplore.ieee.org/document/9163090/

Internet

https://ieeexplore.ieee.org/document/9163090/

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