{"title":"Lossless compression of high-frequency voltage and current data in smart grids","authors":"A. Unterweger, D. Engel","doi":"10.1109/BigData.2016.7840968","DOIUrl":null,"url":null,"abstract":"In smart grids, both, low-frequency and high-frequency measurements are performed in households for a variety of use cases. While several compressibility studies of this data have been conducted in the literature, lossless compression of high-frequency data has not yet been covered. In this paper, high-frequency voltage and current data is processed with a selection of low-complexity compression algorithms to find that the data are not equally compressible. Further, it is found that the compression performance varies with resolution as well as between households and data sets. Nonetheless, the use of compression is practically viable for the current channels of the evaluated data sets at 16 and 50 kHz, respectively.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":"81 1","pages":"3131-3139"},"PeriodicalIF":2.6000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/BigData.2016.7840968","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 7
Abstract
In smart grids, both, low-frequency and high-frequency measurements are performed in households for a variety of use cases. While several compressibility studies of this data have been conducted in the literature, lossless compression of high-frequency data has not yet been covered. In this paper, high-frequency voltage and current data is processed with a selection of low-complexity compression algorithms to find that the data are not equally compressible. Further, it is found that the compression performance varies with resolution as well as between households and data sets. Nonetheless, the use of compression is practically viable for the current channels of the evaluated data sets at 16 and 50 kHz, respectively.
Big DataCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍：
Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.
Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.
Big Data coverage includes:
Big data industry standards,
New technologies being developed specifically for big data,
Data acquisition, cleaning, distribution, and best practices,
Data protection, privacy, and policy,
Business interests from research to product,
The changing role of business intelligence,
Visualization and design principles of big data infrastructures,
Physical interfaces and robotics,
Social networking advantages for Facebook, Twitter, Amazon, Google, etc,
Opportunities around big data and how companies can harness it to their advantage.
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