Ullah Miah, Md Saef and Islam, Md Imamul and Islam, Saiful and Ahmed, Ahanaf and Rahman, Mushfiqur Mahabubur and Mahmud, Mufti R. (2024) Sustainability-driven hourly energy demand forecasting in Bangladesh using Bi-LSTMs. In: Procedia Computer Science. 2023 International Symposium on Green Technologies and Applications, ISGTA 2023 , 27-29 December 2023 , Casablanca. pp. 41-50., 236. ISSN 1877-0509 (Published)
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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.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Bi-LSTM; Deep learning; Energy demand prediction; Short term demand forecasting |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 31 Jul 2024 03:30 |
| Last Modified: | 31 Jul 2024 03:30 |
| URI: | http://umpir.ump.edu.my/id/eprint/41731 |
| Download Statistic: | View Download Statistics |
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