Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is pr...

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Veröffentlicht in:	Frontiers in energy research Jg. 11
Hauptverfasser:	Alhussan, Amel Ali, M. El-Kenawy, El-Sayed, Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Frontiers Media S.A 01.06.2023
Schlagworte:	bidirectional long short-term memory dipper throated optimization genetic algorithm machine learning metaheuristic optimization wind speed forecasting
ISSN:	2296-598X, 2296-598X
Online-Zugang:	Volltext
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