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

Author:

Sulaiman Mohd HerwanORCID,Mustaffa ZurianiORCID

Funder

UMPSA

Malaysia Ministry of Higher Education

Publisher

Elsevier BV

Reference49 articles.

1. COA-CNN-LSTM: coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications;Abou Houran;Appl. Energy,2023

2. An improved metaheuristic method-based neural network for predicting wind turbine power;Ahilan;Cybern. Syst.,2023

3. Wind energy assessment using weibull distribution with different numerical estimation methods: a case study;Alanazi;Emerg. Sci. J.,2023

4. Machine learning-based time series modelling for large-scale regional wind power forecasting: a case study in Ontario, Canada;Alkabbani;Cleaner Energy Syst.,2023

5. Adama II wind farm long-term power generation forecasting based on machine learning models;Ayele;Sci. Afr.,2023

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