Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization

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Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization

Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real data...

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Bibliographic Details
Main Authors:	Mohd Herwan, Sulaiman, Zuriani, Mustaffa
Format:	Article
Language:	English
Published:	Elsevier B.V. 2024
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/42920/ http://umpir.ump.edu.my/id/eprint/42920/1/Enhancing%20wind%20power%20forecasting%20accuracy%20with%20hybrid.pdf

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