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

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecti...

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Main Authors: AL-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Paw J.K.S., Singh M.J.
Other Authors: 57212194331
Format: Review
Published: MDPI 2024
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spelling my.uniten.dspace-342612024-10-14T11:18:42Z Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations AL-Jumaili A.H.A. Muniyandi R.C. Hasan M.K. Paw J.K.S. Singh M.J. 57212194331 14030355800 55057479600 58168727000 58765817900 big data cloud computing data mining parallel computing power system Cloud analytics Computational efficiency Computer architecture Computing power Data mining Green computing Information management Parallel processing systems Parallel programming Power management Cloud-computing Data analytics Parallel com- puting Power Power management systems Power system Process delay Process time System conditions System status Big data Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges. � 2023 by the authors. Final 2024-10-14T03:18:42Z 2024-10-14T03:18:42Z 2023 Review 10.3390/s23062952 2-s2.0-85151433401 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151433401&doi=10.3390%2fs23062952&partnerID=40&md5=650f5518d5807ee40bbf001e64c4fd79 https://irepository.uniten.edu.my/handle/123456789/34261 23 6 2952 All Open Access Gold Open Access MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic big data
cloud computing
data mining
parallel computing
power system
Cloud analytics
Computational efficiency
Computer architecture
Computing power
Data mining
Green computing
Information management
Parallel processing systems
Parallel programming
Power management
Cloud-computing
Data analytics
Parallel com- puting
Power
Power management systems
Power system
Process delay
Process time
System conditions
System status
Big data
spellingShingle big data
cloud computing
data mining
parallel computing
power system
Cloud analytics
Computational efficiency
Computer architecture
Computing power
Data mining
Green computing
Information management
Parallel processing systems
Parallel programming
Power management
Cloud-computing
Data analytics
Parallel com- puting
Power
Power management systems
Power system
Process delay
Process time
System conditions
System status
Big data
AL-Jumaili A.H.A.
Muniyandi R.C.
Hasan M.K.
Paw J.K.S.
Singh M.J.
Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
description Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges. � 2023 by the authors.
author2 57212194331
author_facet 57212194331
AL-Jumaili A.H.A.
Muniyandi R.C.
Hasan M.K.
Paw J.K.S.
Singh M.J.
format Review
author AL-Jumaili A.H.A.
Muniyandi R.C.
Hasan M.K.
Paw J.K.S.
Singh M.J.
author_sort AL-Jumaili A.H.A.
title Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
title_short Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
title_full Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
title_fullStr Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
title_full_unstemmed Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
title_sort big data analytics using cloud computing based frameworks for power management systems: status, constraints, and future recommendations
publisher MDPI
publishDate 2024
_version_ 1814061112731107328
score 13.214268