Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model

This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional...

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Autors principals:	Zachary Barbre, Gang Li
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-04-01
Col·lecció:	Algorithms
Matèries:	wind energy forecasting time series prediction recurrent neural networks long short-term memory extended long short-term memory
Accés en línia:	https://www.mdpi.com/1999-4893/18/4/206
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