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Updated: Aug 5, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and

Ahmed Hadi Ali Al-Jumaili^1,2, Ravie Chandren Muniyandi¹, Mohammad Kamrul Hasan¹

¹Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

Cloud computing addresses challenges in power management systems, improving data mining and real-time monitoring. This approach enhances efficiency and overcomes limitations of traditional parallel processing for big data analysis.

Keywords:

big data cloud computing data mining parallel computing power system

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Published on: November 22, 2019

Area of Science:

Computer Science
Electrical Engineering
Data Science

Background:

Traditional parallel computing faces challenges in power management systems, including execution time, complexity, and delays in condition monitoring.
Data management is a critical bottleneck in processing diverse power system data like consumption, weather, and generation.
Existing methods struggle with the scale and speed required for effective data mining and prediction.

Purpose of the Study:

To review cloud computing architectures for efficient data management in power systems.
To analyze cloud solutions within the context of big data and emerging parallel programming models.
To propose a new design concept for real-time big data management in power systems.

Main Methods:

Review of cloud computing concepts and architectures for power system monitoring.
Discussion of cloud solutions for big data, including Hadoop, Spark, and Storm.
Modeling of key performance metrics for cloud computing applications in big data analysis.

Main Results:

Cloud computing offers a viable solution to overcome the limitations of traditional parallel processing in power management.
Emerging parallel programming models show advancements and constraints in handling big data.
The study models key performance metrics for competitiveness in big data analysis.

Conclusions:

Cloud computing architectures can meet multi-level real-time requirements for improved power system monitoring and performance.
Recommendations are provided for cloud computing infrastructure and methods for managing real-time big data.
The proposed design concept aims to solve data mining challenges in power management systems.