Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning

Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning

ABSTRACT Regional‐scale holistic wind power prediction (WPP) is pivotal to securing the safety, stability, and economic efficiency of power systems. To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fin...

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Bibliographic Details
Main Authors: Guoyuan Qin, Xiaosheng Peng, Zimin Yang
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Wind Energy
Subjects:
bidirectional long short‐term memory neural network
deep learning
empirical mode decomposition
error compensation
fine‐to‐coarse
regional wind power prediction
Online Access:https://doi.org/10.1002/we.70025
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https://doi.org/10.1002/we.70025

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