@article{MIAH202441,
title = {Sustainability-Driven Hourly Energy Demand Forecasting in Bangladesh Using Bi-LSTMs},
journal = {Procedia Computer Science},
volume = {236},
pages = {41-50},
year = {2024},
note = {International Symposium on Green Technologies and Applications (ISGTA’2023)},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2024.05.002},
url = {https://www.sciencedirect.com/science/article/pii/S1877050924010184},
author = {Md Saef Ullah Miah and Md. Imamul Islam and Saiful Islam and Ahanaf Ahmed and M. Mostafizur Rahman and Mufti Mahmud},
keywords = {Energy demand prediction, Deep learning, Short term demand forecasting, Bi-LSTM},
abstract = {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.}
}

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M.","Mahmud, M."],"bibdata":{"bibtype":"article","type":"article","title":"Sustainability-Driven Hourly Energy Demand Forecasting in Bangladesh Using Bi-LSTMs","journal":"Procedia Computer Science","volume":"236","pages":"41-50","year":"2024","note":"International Symposium on Green Technologies and Applications (ISGTA’2023)","issn":"1877-0509","doi":"https://doi.org/10.1016/j.procs.2024.05.002","url":"https://www.sciencedirect.com/science/article/pii/S1877050924010184","author":[{"firstnames":["Md","Saef","Ullah"],"propositions":[],"lastnames":["Miah"],"suffixes":[]},{"firstnames":["Md.","Imamul"],"propositions":[],"lastnames":["Islam"],"suffixes":[]},{"firstnames":["Saiful"],"propositions":[],"lastnames":["Islam"],"suffixes":[]},{"firstnames":["Ahanaf"],"propositions":[],"lastnames":["Ahmed"],"suffixes":[]},{"firstnames":["M.","Mostafizur"],"propositions":[],"lastnames":["Rahman"],"suffixes":[]},{"firstnames":["Mufti"],"propositions":[],"lastnames":["Mahmud"],"suffixes":[]}],"keywords":"Energy demand prediction, Deep learning, Short term demand forecasting, Bi-LSTM","abstract":"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.","bibtex":"@article{MIAH202441,\ntitle = {Sustainability-Driven Hourly Energy Demand Forecasting in Bangladesh Using Bi-LSTMs},\njournal = {Procedia Computer Science},\nvolume = {236},\npages = {41-50},\nyear = {2024},\nnote = {International Symposium on Green Technologies and Applications (ISGTA’2023)},\nissn = {1877-0509},\ndoi = {https://doi.org/10.1016/j.procs.2024.05.002},\nurl = {https://www.sciencedirect.com/science/article/pii/S1877050924010184},\nauthor = {Md Saef Ullah Miah and Md. Imamul Islam and Saiful Islam and Ahanaf Ahmed and M. Mostafizur Rahman and Mufti Mahmud},\nkeywords = {Energy demand prediction, Deep learning, Short term demand forecasting, Bi-LSTM},\nabstract = {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.}\n}","author_short":["Miah, M. S. U.","Islam, M. I.","Islam, S.","Ahmed, A.","Rahman, M. 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