Nahid, F.A., & Roy, J. (2025). PyPSA-BD: A customized model to explore decarbonized energy transition for developing country. Renewable Energy Focus, 100655. https://doi.org/10.1016/j.ref.2024.100655
Nahid, F.A., Ongsakul, W., Singh, J.G. et al. (2024). Short-term customer-centric electric load forecasting for low carbon microgrids using a hybrid model. Energy Systems. https://doi.org/10.1007/s12667-024-00704-5
Nahid, F.A., Ongsakul, W., Manjiparambil, N.M., Singh, J.G., Roy, J. (2023). Mode decomposition-based short-term multi-step hybrid solar forecasting model for microgrid applications. Electrical Engineering. https://doi.org/10.1007/s00202-023-02138-1
Nahid, F. A., Ongsakul, W., & Manjiparambil, N. M. (2023). Short-term multi-step wind speed forecasting for carbon neutral microgrid by a decomposition-based hybrid model. Energy for Sustainable Development, 73, 87–100. https://doi.org/10.1016/j.esd.2023.01.016
Nahid, F. A., Chowdhury, H. M., & Jahangir, M. N. (2019). Solar Radiation Forecasting Using Hybrid Convolutional Long Short-Term Memory Neural Network. Journal of Research in Physics and Applied Sciences, 2(2), 1–13. https://doi.org/10.5281/zenodo.3768721
Nahid, F. A., Alam, M. J., & Akter, K. (2019). Multi Step Ahead Wind Speed Forecasting Using Long Short-Term Memory Recurrent Neural Network. IUBAT Review, 2(1), 31–40. https://iubat.edu/journal
Akter, K., Nahid, F. A., & Islam, N. (2019). Open Loop Analysis of a High-Performance Input Switched Single Phase AC-DC Boost Converter. Journal of Electrical and Electronics Engineering, 6(7), 6–11. https://doi.org/10.14445/23488379/IJEEE-V6I7P102
Alam, M. J., Nahid, F. A., & Islam, M. T. (2019). Design of a Broad Band–Stop Filter with Metamaterial as Defective Ground System. IUBAT Review, 2(1), 41–48. https://iubat.edu/journal
Nahid, F.A., J. Roy, A. Barua and W. Ongsakul (2024). "PyPSA-BD: An Open-Source Model for Planning Sustainable Power Sector for Bangladesh," 2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE), Pattaya City, Thailand, pp. 1-7. https://doi.org/10.1109/ICUE63019.2024.10795639
A. Barua, W. Ongsakul, F. A. Nahid and J. Roy (2024). "Comparative Analysis of Energy System Modeling Approaches for Decarbonizing the Electricity Sector," 2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE), Pattaya City, Thailand, pp. 1-7. https://doi.org/10.1109/ICUE63019.2024.10795621
Nahid, F. A., Ongsakul, W., & Manjiparambil, N. M. (2020). Very Short-Term Wind Speed Forecasting Using Convolutional Long Short-Term Memory Recurrent Neural Network. 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE), pp. 1–8. https://doi.org/10.1109/ICUE49301.2020.9307061
Akter, K., Islam, M. N., Nahid, F. A., & Soheli, S. N. (2021). Comparative Analysis and Exploration of a High Gain Input Current Shaped AC-DC Step-Up Converter with Feedback Controller. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 253–258. https://doi.org/10.1109/ICREST51555.2021.9331180
Nahid, F. A., Jahangir, M. N., Chowdhury, H. M., & Akter, K. Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price. In: Forecasting in Smart Grids, pp. 173–218. https://doi.org/10.1002/9781394249466.ch7
Nahid, F. A., Chowdhury, H. M., & Jahangir, M. N. Machine Learning Techniques for Demand Forecasting in the Electricity Sector. In: Forecasting in Smart Grids, pp. 131–172. https://doi.org/10.1002/9781394249466.ch6
Nahid, F. A., Ongsakul, W., Madhu M., N., & Laopaiboon, T. (2020). Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN. In Vasant, P., Weber, G., & Punurai, W. (Eds.), Research Advancements in Smart Technology, Optimization, and Renewable Energy (pp. 200–222). IGI Global. http://doi:10.4018/978-1-7998-3970-5.ch011