A Global Data Model for Electric Power Data Centers

Wang, Zhiqiang; Bai, Wanjian; Dong, Aiqiang; Zhang, FuHua; Li, Bo

doi:10.1007/s11265-019-01474-5

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…However, with the increasing informatization of the power system and the continuous quantification of power data, the application scope of data mining technology continues to expand. Parallel algorithms can be designed in more aspects to enhance the data processing effect and carry out all around power system production and dispatching [ 223 , 224 ]; To design a comprehensive cloud computing platform architecture based on cloud computing and big data processing technology, which provides a reference plan for the construction of the computing platform of the power equipment monitoring center [ 225 , 226 ]; To improve the real-time response speed of online monitoring data of power equipment, a real-time processing framework for streaming data based on Storm is needed, and an incremental variable prediction model classification method [ 227 , 228 , 229 ]. …”

Section: Future Trendmentioning

confidence: 99%

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

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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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.

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Section: Future Trendmentioning

confidence: 99%

“…To design a comprehensive cloud computing platform architecture based on cloud computing and big data processing technology, which provides a reference plan for the construction of the computing platform of the power equipment monitoring center [ 225 , 226 ];…”

Section: Future Trendmentioning

confidence: 99%

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

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The power consumption models of different components of the IT load section and the IPSS are presented in [18]- [20], however, the models are limited to identify the component level consumption and losses considering the dynamic structures and topologies of DCs. The modular modeling approaches can consider the dynamic structures of the load sections for power consumption modeling from component-level to aggregated-level [6], [21], [22].…”

Section: B Power Consumption Modeling Of It Loads and Power Conditioning Devicesmentioning

confidence: 99%

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

2021

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The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.INDEX TERMS Adequacy, data center, energy losses, internal power supply system, reliability.

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Research on Application and Development of Intelligent Video Recognition Technology in Power Transmission, Transformer and Distribution System

Wang

Cai

et al. 2023

2023 Asia-Europe Conference on Electronics, Data Processing and Informatics (ACEDPI)

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A Global Data Model for Electric Power Data Centers

Cited by 3 publications

References 18 publications

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

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

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

Research on Application and Development of Intelligent Video Recognition Technology in Power Transmission, Transformer and Distribution System

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