Outlier Data Treatment Methods Toward Smart Grid Applications
In a smart grid environment, advanced metering infrastructure (AMI) and intelligent sensors have been deployed extensively. As a result, large-scale and fine-grained smart grid data are more convenient to be collected, in which outliers exist pervasively, caused by system failures, environmental eff...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8404086/ |
| _version_ | 1819169228427624448 |
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| author | Li Sun Kaile Zhou Xiaoling Zhang Shanlin Yang |
| author_facet | Li Sun Kaile Zhou Xiaoling Zhang Shanlin Yang |
| author_sort | Li Sun |
| collection | DOAJ |
| description | In a smart grid environment, advanced metering infrastructure (AMI) and intelligent sensors have been deployed extensively. As a result, large-scale and fine-grained smart grid data are more convenient to be collected, in which outliers exist pervasively, caused by system failures, environmental effects, and human interventions. Outlier deletion is always implemented in data preprocessing for improving data quality. However, due to the fact that real records that reflect rare and unusual patterns are also recognized as outliers, outlier mining is necessary to be carried out with the aim of discovering knowledge on abnormal patterns in power generation, transmission, distribution, transformation, and consumption. To the best of our knowledge, a comprehensive and systematic review of outlier data treatment methods is still lacked in the smart grid environment. We, in this paper, aim at presenting the review of outlier data treatment methods toward smart grid applications and categorize them into outlier rejection and outlier mining groups. Since we do this survey from the perspective of data-driven analytics and data mining methods, information security technologies are barely discussed in this paper. Based on a general overview of outlier data treatment methods, we make the contribution of providing the application scenarios of outlier rejection and outlier mining in the smart grid environment. With the construction of smart grid throughout the world, dealing with outlier data has become more crucial for the security and reliability of power system operation. Therefore, we also discuss some future challenges of outlier data treatment toward smart energy management. |
| first_indexed | 2024-12-22T19:16:10Z |
| format | Article |
| id | doaj.art-09a8abd87d254bb3ae149fce362dab03 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:16:10Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-09a8abd87d254bb3ae149fce362dab032022-12-21T18:15:31ZengIEEEIEEE Access2169-35362018-01-016398493985910.1109/ACCESS.2018.28527598404086Outlier Data Treatment Methods Toward Smart Grid ApplicationsLi Sun0Kaile Zhou1https://orcid.org/0000-0001-5355-1322Xiaoling Zhang2Shanlin Yang3School of Management, Hefei University of Technology, Hefei, ChinaSchool of Management, Hefei University of Technology, Hefei, ChinaDepartment of Public Policy, City University of Hong Kong, Hong KongSchool of Management, Hefei University of Technology, Hefei, ChinaIn a smart grid environment, advanced metering infrastructure (AMI) and intelligent sensors have been deployed extensively. As a result, large-scale and fine-grained smart grid data are more convenient to be collected, in which outliers exist pervasively, caused by system failures, environmental effects, and human interventions. Outlier deletion is always implemented in data preprocessing for improving data quality. However, due to the fact that real records that reflect rare and unusual patterns are also recognized as outliers, outlier mining is necessary to be carried out with the aim of discovering knowledge on abnormal patterns in power generation, transmission, distribution, transformation, and consumption. To the best of our knowledge, a comprehensive and systematic review of outlier data treatment methods is still lacked in the smart grid environment. We, in this paper, aim at presenting the review of outlier data treatment methods toward smart grid applications and categorize them into outlier rejection and outlier mining groups. Since we do this survey from the perspective of data-driven analytics and data mining methods, information security technologies are barely discussed in this paper. Based on a general overview of outlier data treatment methods, we make the contribution of providing the application scenarios of outlier rejection and outlier mining in the smart grid environment. With the construction of smart grid throughout the world, dealing with outlier data has become more crucial for the security and reliability of power system operation. Therefore, we also discuss some future challenges of outlier data treatment toward smart energy management.https://ieeexplore.ieee.org/document/8404086/Outlier data treatmentoutlier rejectionoutlier miningsmart griddata preprocessing |
| spellingShingle | Li Sun Kaile Zhou Xiaoling Zhang Shanlin Yang Outlier Data Treatment Methods Toward Smart Grid Applications IEEE Access Outlier data treatment outlier rejection outlier mining smart grid data preprocessing |
| title | Outlier Data Treatment Methods Toward Smart Grid Applications |
| title_full | Outlier Data Treatment Methods Toward Smart Grid Applications |
| title_fullStr | Outlier Data Treatment Methods Toward Smart Grid Applications |
| title_full_unstemmed | Outlier Data Treatment Methods Toward Smart Grid Applications |
| title_short | Outlier Data Treatment Methods Toward Smart Grid Applications |
| title_sort | outlier data treatment methods toward smart grid applications |
| topic | Outlier data treatment outlier rejection outlier mining smart grid data preprocessing |
| url | https://ieeexplore.ieee.org/document/8404086/ |
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