Multi-source deep transfer learning with stacked denoising autoencoder and Wasserstein distance for wind power prediction in new wind farm

•Overcome the challenge of insufficient historical data in newly established wind farms.•Stacked denoising autoencoder is used to establish feature correlations between the source and target domain.•The parameters of LSTM prediction model from a source wind farm are transferred to the target farm’s...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 258; S. 119225
Hauptverfasser:	Xu, Haiyan, Hao, Wenguang, Zhao, Yong, Tian, Hongda
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 30.01.2026
Schlagworte:	Stacked denoising autoencoder Transfer learning Wasserstein distance Wind power forecasting Wasserstein distance Wind power forecasting Transfer learning Stacked denoising autoencoder
ISSN:	0263-2241
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!