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

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

Figures & Tables

Figure 1.

Architecture IoT connected objects [37].
Architecture IoT connected objects [37].

Figure 2.

Programming the MCU Board.
Programming the MCU Board.

Figure 3.

Intelligent building planning.
Intelligent building planning.

Figure 4.

Server interface to control the objects.
Server interface to control the objects.

Figure 5.

World energy consumption.
World energy consumption.

Figure 6.

General architecture of smart buildings.
General architecture of smart buildings.

Figure 7.

Overall energy consumption per month.
Overall energy consumption per month.

Figure 8.

Information about the data.
Information about the data.

Figure 9.

Consumption for devices.
Consumption for devices.

Figure 10.

Home activity in day 2021-10-4.
Home activity in day 2021-10-4.
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,
Spark MLlib,
Machine Learning,
Predictive Modeling,
Grid Stability,
Energy Forecasting,
Data Analysis
Related subjects:
Computer sciences,
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.

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