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

• 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/42920/1/Enhancing%20wind%20power%20forecasting%20accuracy%20with%20hybrid.pdf; http://umpir.ump.edu.my/id/eprint/42920/; https://doi.org/10.1016/j.cles.2024.100139; https://doi.org/10.1016/j.cles.2024.100139

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...