A Hybrid LSTM Framework for Short-Term Regional Wind Speed Forecasting Based on PCA and SSA-Optimized VMD

Accurate regional wind speed forecasting is critical yet challenging due to inherent spatiotemporal correlations and data non-stationarity. This paper proposes a hybrid framework combining Principal Component Analysis (PCA), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) net...

詳細記述

書誌詳細

主要な著者:	Huachen Li, Zhengzheng Ma, Liang Chen, Qinglin Zhu, Xiang Dong, Bin Xu, Yuanming Li, Mantong Zhang
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2026-04-01
シリーズ:	Applied Sciences
主題:	regional wind speed forecasting multi-step forecasting spatiotemporal feature extraction Variational Mode Decomposition (VMD) Principal Component Analysis (PCA) Sparrow Search Algorithm (SSA)
オンライン･アクセス:	https://www.mdpi.com/2076-3417/16/9/4225
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!