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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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 |
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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 |
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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 |
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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 |
13.222552 |