Sustainability-driven hourly energy demand forecasting in Bangladesh using Bi-LSTMs

• Main Authors: Ullah Miah, Md Saef, Islam, Md Imamul, Islam, Saiful, Ahmed, Ahanaf, Rahman, Mushfiqur Mahabubur, Mahmud, Mufti R.
• Format: Conference or Workshop Item
• Language: English
• Published: Elsevier B.V. 2024
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/41731/1/Sustainability-driven%20hourly%20energy%20demand%20forecasting%20in%20Bangladesh%20using%20Bi-LSTMs.pdf; http://umpir.ump.edu.my/id/eprint/41731/; https://doi.org/10.1016/j.procs.2024.05.002
• Institution: Universiti Malaysia Pahang Al-Sultan Abdullah

This research presents a comprehensive study on developing and evaluating a deep learning-based forecasting model for hourly energy demand prediction in Bangladesh. Leveraging a novel dataset obtained from the Power Grid Company of Bangladesh (PGCB), the proposed model utilizes bi-directional long s...