no code implementations • 7 Apr 2023 • Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency.
no code implementations • 28 Mar 2023 • Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, Nasser Hosseizadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years.
no code implementations • 29 Sep 2022 • Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few.
no code implementations • 21 Mar 2022 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud
In this paper, we propose an improved Bayesian bidirectional long-short term memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting.
no code implementations • 1 Mar 2022 • Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development.
no code implementations • 28 Nov 2021 • Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
Therefore, this paper presents a comprehensive survey of the recent advances in FDI attacks within active distribution systems and proposes a taxonomy to classify the FDI threats with respect to smart grid targets.
no code implementations • 24 Mar 2021 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, Md. Enamul Haque, Adnan Anwar
This paper proposes a novel Bayesian probabilistic technique for forecasting renewable solar generation by addressing data and model uncertainties by integrating bidirectional long short-term memory (BiLSTM) neural networks while compressing the weight parameters using variational autoencoder (VAE).
no code implementations • 25 Nov 2020 • Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, Md. Enamul Haque, ZhaoYang Dong
Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch.