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

Al-Jumaili, Ahmed Hadi Ali ; Muniyandi, Ravie Chandren ; Hasan, Mohammad Kamrul ; Paw, Johnny Koh Siaw ; Singh, Mandeep Jit

Sensors (Basel, Switzerland), 2023-03, Vol.23 (6), p.2952 [Periódico revisado por pares]

Switzerland: MDPI AG

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Título:
Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
Autor: Al-Jumaili, Ahmed Hadi Ali ; Muniyandi, Ravie Chandren ; Hasan, Mohammad Kamrul ; Paw, Johnny Koh Siaw ; Singh, Mandeep Jit
Assuntos: Algorithms ; Batch processing ; Big Data ; Cloud computing ; Communication ; Computers ; Condition monitoring ; Data analysis ; Data mining ; Data processing ; Data sampling ; Electric power ; Electric power production ; Electric power systems ; Electricity distribution ; Germany ; Information processing ; Infrastructure (Economics) ; Malaysia ; Management systems ; Meteorological data ; Monitoring systems ; Multiprocessing ; parallel computing ; Parallel processing ; Parallel programming ; Performance measurement ; Power consumption ; Power management ; power system ; Real time ; Review ; Software
É parte de: Sensors (Basel, Switzerland), 2023-03, Vol.23 (6), p.2952
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
Descrição: 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.
Editor: Switzerland: MDPI AG
Idioma: Inglês

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

Al-Jumaili, Ahmed Hadi Ali ; Muniyandi, Ravie Chandren ; Hasan, Mohammad Kamrul ; Paw, Johnny Koh Siaw ; Singh, Mandeep Jit

Switzerland: MDPI AG

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