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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (6), p.2952
Main Authors: Al-Jumaili, Ahmed Hadi Ali, Muniyandi, Ravie Chandren, Hasan, Mohammad Kamrul, Paw, Johnny Koh Siaw, Singh, Mandeep Jit
Format: Article
Language:English
Subjects:
Algorithms
Batch processing
Big Data
Cloud computing
Communication
Computers
Data analysis
Data mining
Data processing
Data sampling
Electric power
Electric power production
Electric power systems
Electricity distribution
Germany
Information processing
Infrastructure (Economics)
Malaysia
Meteorological data
Monitoring systems
Multiprocessing
parallel computing
Parallel processing
Parallel programming
Power management
power system
Real time
Review
Software
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23062952