.png){kind=icon}
返回
Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations
.png){kind=icon}
OA
AI
DOI：10.3390/s23062952.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
data mining
big data
cloud computing
parallel computing
power system
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
A comparative study of local quantum Fisher information and local quantum uncertainty in Heisenberg XY model海森堡XY模型中局部量子Fisher信息和局部量子不确定性的比较研究OAAI
OAAI分享收藏Customizable Computing-From Single Chip to Datacenters可定制的计算-从单芯片到数据中心.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏
Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems电池数字双胞胎: 智能电池管理系统的模型，数据和人工智能融合的观点ENERGY AND AIOAAI
ENERGY AND AIIF9.6
OAAI分享收藏
查看更多内容