Big Data Metadata Management in Smart Grids

Nguyen, Trinh Hoang; Prinz, Andreas

doi:10.1007/978-3-319-05029-4_8

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…erature, such as [11][12][13][14][15][16], in being the first meta-analytic review on efficient SG data processing with focus on DEM. Besides, it gives useful insights into technologies and methods from the area of big data analytics (BDA) that have to be further explored into the framework of DEM, demand forecasting, and dynamic pricing.…”

Section: Introductionmentioning

confidence: 99%

“…The efficient processing of the produced vast amount of data requires increased data storage and computing resources, which imply the need for high performance computing (HPC) techniques. erature, such as [11][12][13][14][15][16], in being the first meta-analytic review on efficient SG data processing with focus on DEM. Besides, it gives useful insights into technologies and methods from the area of big data analytics (BDA) that have to be further explored into the framework of DEM, demand forecasting, and dynamic pricing.…”

Section: Introductionmentioning

confidence: 99%

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Big Data Analytics for Dynamic Energy Management in Smart Grids

2015

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The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of the large volumes of data generated by a vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.Comment: Published in ELSEVIER Big Data Researc

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Dynamic Energy Management in Smart Grids

2015

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“…DEM estimates the electricity cost and sets corrects the prices of electricity by enabling and interrelating to the energy demands and the prices of the electricity [28]. Some of the frequent surveys in [11][12][13][14][15][16], provides the most and first meta-analytic analysis for data processing that focuses on Dynamic Energy management using Smart Grid. The Big Data analytics (BDA) provides observations into technologiesfrom the framework of Dynamic Energy Management and also dynamic pricing.…”

Section: Literature Surveymentioning

confidence: 99%

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Lydia¹,

Kumar²,

Ramya³

2018

IJET

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The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.

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“…This issue has been addressed in [16], which proposes a deterministic filter technique for outliers detection in wind speed data, employing the obtained data for designing a wind power forecasting module based on a probabilistic neural network. Similarly, the detection of outliers can be addressed by using the ontology approach for managing information in big data as proposed by [17], where this method allows management of the off shore wind farm data by building a hierarchical classification, from which it is possible to compute several derived quantities in order to make a comparison with the measured outliers by starting from the others available measured data through applying known mathematical relations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

De¹,

Vaccaro

Villacci

2018

Electronics

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Abstract:The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling

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Big Data Metadata Management in Smart Grids

Cited by 9 publications

References 31 publications

Big Data Analytics for Dynamic Energy Management in Smart Grids

Big Data Analytics for Dynamic Energy Management in Smart Grids

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Adaptive Wind Generation Modeling by Fuzzy Clustering of Experimental Data

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