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

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

doi:10.14313/jamris-2025-036

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

Journal of Automation, Mobile Robotics and Intelligent Systems

Volume 19 (2025): Issue 4 (December 2025)

By: Amal Zouhri, Ismail Boumhidi, Ismail Boumhidi, Abderahamane Ez-Zahout, Said Chakouk and Mostafa El Mallahi

Open Access

|Dec 2025

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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DOI: https://doi.org/10.14313/jamris-2025-036 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649

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Language: English

Page range: 70 - 81

Submitted on: Apr 28, 2023

Accepted on: Sep 4, 2024

Published on: Dec 24, 2025

Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Smart Grid,

Stability Prediction,

Renewable Energy Resources,

Related subjects:

Artificial intelligence,

Engineering,

Electrical engineering,

Control engineering, metrology and testing,

Mechanical engineering,

Fundamentals of mechanical engineering

© 2025 Amal Zouhri, Ismail Boumhidi, Ismail Boumhidi, Abderahamane Ez-Zahout, Said Chakouk, Mostafa El Mallahi, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2025): Issue 4 (December 2025)