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 short-term memory networks (Bi-LSTMs), implemented through Tensor-Flow and Keras libraries. The study meticulously preprocesses the data, handling missing values and ensuring compatibility with the selected models. The models are trained and evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics, revealing promising results of 376.72 of MAE. The experimental findings demonstrate the effectiveness of the developed forecasting model, showcasing its capability to predict energy demand accurately. The insights derived from this study pave the way for enhanced energy management strategies, fostering sustainable and efficient energy utilization practices.