Short-Term Wind Power Forecasting on Multiple Scales Using VMD Decomposition, K-Means Clustering and LSTM Principal Computing

Short-Term Wind Power Forecasting on Multiple Scales Using VMD Decomposition, K-Means Clustering and LSTM Principal Computing

Wind power plays a crucial role in the secure conversion and management of the power system. Therefore, this study proposes a hybrid model for short-term wind power forecasting, which consists of the variational mode decomposition(VMD), the K-means clustering algorithm and long short term memory(LST...

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Bibliographic Details
Main Authors: Zexian Sun, Shenshen Zhao, Jingxuan Zhang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Wind power forecasting
variational mode decomposition
K-means clustering
long short term memory network
Online Access:https://ieeexplore.ieee.org/document/8843987/

Internet

https://ieeexplore.ieee.org/document/8843987/

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