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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Veröffentlicht in:	IEEE access Jg. 8; S. 1
Hauptverfasser:	Dong, Weichao, Sun, Hexu, Li, Zheng, Zhang, Jingxuan, Yang, Huifang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptive algorithms Algorithms Artificial neural networks Back propagation Back propagation networks Cluster analysis Clustering Clustering algorithms Data models Decomposition Economic development Economic forecasting Forecasting K-means clustering Mathematical analysis Mathematical model Mathematical models Mathematical morphology Model accuracy Morphology multiscale mathematical morphological decomposition Neural networks Noise reduction Performance evaluation Predictive models short-term wind-speed forecasting stacked denoising autoencoders Time domain analysis Vector quantization Wavelet transforms Wind power Wind power generation Wind speed Wind turbines
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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