Abstract
The term “smart grid” refers to an innovative network for electricity distribution that employs demand-and-response and bidirectional data exchanges. Therefore, predicting the grid’s stability is crucial to make the smart grid more dependable and the electricity supply more efficient and consistent. This study’s primary objective and contribution were to develop a highly accurate XGBoost model that leverages the Genetic Algorithm as a parameters tuner to predict the stability of smart grids. The proposed model outperformed the other models (Artificial Neural Network, Random Forest, and LightGBM) with a precision of 98.02%.
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Boutahir, M.K., Hessane, A., Farhaoui, Y., Azrour, M. (2023). An Effective Ensemble Learning Model to Predict Smart Grid Stability Using Genetic Algorithms. In: Mabrouki, J., Mourade, A., Irshad , A., Chaudhry, S. (eds) Advanced Technology for Smart Environment and Energy. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-25662-2_11
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