Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction

• Main Authors: Musa, Wahab, Ku Mahamud, Ku Ruhana, Salim, Sardi, Sediyono, Agung
• Format: Article
• Language: English
• Published: Universiti Utara Malaysia Press 2024
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: https://repo.uum.edu.my/id/eprint/30347/1/JICT%2023%2001%202024%2025-47.pdf; https://doi.org/10.32890/jict2024.23.1.2; https://repo.uum.edu.my/id/eprint/30347/; https://e-journal.uum.edu.my/index.php/jict/article/view/20064; https://doi.org/10.32890/jict2024.23.1.2

Energy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However,...