@inproceedings{808e777cbaa04df9bc64a811871d7665,
title = "Accurate smart-grid stability forecasting based on deep learning: Point and interval estimation method",
abstract = "The power grid stability is highly impacted by the fluctuating nature of renewable energy sources. This paper proposes a deep learning method-based bidirectional gated recurrent unit for smart grid stability prediction. For automatic tuning, this study employs Simulated Annealing algorithm to optimize the selected hyperparameters and enhance the model forecastability. The proposed forecasting model's performance is evaluated using electrical grid stability simulated data set. The proposed method provides an accurate point and interval grid stability prediction. Simulation results are conducted to prove the high performance of the proposed method. Furthermore, comparative analysis is performed to demonstrate the superiority of the proposed strategy over some state-of-the-art available solutions.",
keywords = "Bidirectional mechanism, Deep learning, Gated recurrent unit, Grid stability prediction, Interval prediction, Point",
author = "Mohamed Massaoudi and Haitham Abu-Rub and Refaat, {Shady S.} and Ines Chihi and Oueslati, {Fakhreddine S.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2nd Annual IEEE Kansas Power and Energy Conference, KPEC 2021 ; Conference date: 19-04-2021 Through 20-04-2021",
year = "2021",
doi = "10.1109/KPEC51835.2021.9446196",
language = "English",
series = "2021 IEEE Kansas Power and Energy Conference, KPEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Kansas Power and Energy Conference, KPEC 2021",
address = "United States",
}