Challenges in Predicting Smart Grid Stability Linked with Renewable Energy Resources Through Spark MLlib Learning

Zouhri, Amal; Boumhidi, Ismail; Boumhidi, Ismail; Ez-Zahout, Abderahamane; Chakouk, Said; Mallahi, Mostafa El

doi:10.14313/jamris-2025-036

Abstract

This article conducts a numerical analysis focused on the predictive stability of smart grids, particularly in connection with renewable energy resources. The study leverages SparkMLlib machine learning tools to develop a predictive model. The aim is to enhance the understanding and forecasting of smart grid stability, with a specific emphasis on the integration of renewable energy sources. The numerical analysis involves the utilization of advanced algorithms and techniques provided by SparkMLlib to assess the intricate relationships among various factors impacting smart grid stability. The findings of this study contribute to the ongoing efforts to optimize the reliability and efficiency of smart grids in the context of increasing reliance on renewable energy resources.

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Challenges in Predicting Smart Grid Stability Linked with Renewable Energy Resources Through Spark MLlib Learning

Abstract

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