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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Bibliographic Details

Published in:	Algorithms
Main Authors:	Zachary Barbre, Gang Li
Format:	Article
Language:	English
Published:	MDPI AG 2025-04-01
Subjects:	wind energy forecasting time series prediction recurrent neural networks long short-term memory extended long short-term memory
Online Access:	https://www.mdpi.com/1999-4893/18/4/206