Academic Journal
Analytics framework for optimal smart meters data processing.
| Title: | Analytics framework for optimal smart meters data processing. |
|---|---|
| Authors: | Alquthami, Thamer, AlAmoudi, Ahmed, Alsubaie, Abdullah M., Jaber, Abdulrahman Bin, Alshlwan, Nassir, Anwar, Murad, Al Husaien, Shafi |
| Source: | Electrical Engineering; Sep2020, Vol. 102 Issue 3, p1241-1251, 11p |
| Subject Terms: | Smart meters, Electronic data processing, Load forecasting (Electric power systems), Visual analytics, Structural frames, Test validity |
| Abstract: | Utilities around the world have realized the importance of wide installation smart meters (SMs) as they are considered to be a corner stone of any step toward grid modernization. These meters are expected to rely on to improve gird reliability, efficiency and enhance grid economic operation. With large rate of SMs integration, flood of smart meter data is being gathered on hourly basis. This paper presents an integrated data framework that incorporates tools and data preprocessing techniques for SM data analytics. This framework uses real data of smart meters installed by the Saudi Electricity Company (SEC) and is for different load profiles, such as residential, governmental, commercial, agriculture and industrial. The developed framework receives raw data from SM, preprocess it and then performs the required analysis using the applications layer. Benefits of such a framework are many: standardized data streamlining, unified different data spectrum and at the end create a trustworthy and validated real-based data that can be used to execute many of smart grid applications. This paper describes the structure of the framework, the function of each component and then presents results of several applications to test the validity and performance of the framework. [ABSTRACT FROM AUTHOR] |
| Copyright of Electrical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s00202-020-00949-0 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Analytics framework for optimal smart meters data processing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alquthami%2C+Thamer%22">Alquthami, Thamer</searchLink><br /><searchLink fieldCode="AR" term="%22AlAmoudi%2C+Ahmed%22">AlAmoudi, Ahmed</searchLink><br /><searchLink fieldCode="AR" term="%22Alsubaie%2C+Abdullah+M%2E%22">Alsubaie, Abdullah M.</searchLink><br /><searchLink fieldCode="AR" term="%22Jaber%2C+Abdulrahman+Bin%22">Jaber, Abdulrahman Bin</searchLink><br /><searchLink fieldCode="AR" term="%22Alshlwan%2C+Nassir%22">Alshlwan, Nassir</searchLink><br /><searchLink fieldCode="AR" term="%22Anwar%2C+Murad%22">Anwar, Murad</searchLink><br /><searchLink fieldCode="AR" term="%22Al+Husaien%2C+Shafi%22">Al Husaien, Shafi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electrical Engineering; Sep2020, Vol. 102 Issue 3, p1241-1251, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Smart+meters%22">Smart meters</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Load+forecasting+%28Electric+power+systems%29%22">Load forecasting (Electric power systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+analytics%22">Visual analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+frames%22">Structural frames</searchLink><br /><searchLink fieldCode="DE" term="%22Test+validity%22">Test validity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Utilities around the world have realized the importance of wide installation smart meters (SMs) as they are considered to be a corner stone of any step toward grid modernization. These meters are expected to rely on to improve gird reliability, efficiency and enhance grid economic operation. With large rate of SMs integration, flood of smart meter data is being gathered on hourly basis. This paper presents an integrated data framework that incorporates tools and data preprocessing techniques for SM data analytics. This framework uses real data of smart meters installed by the Saudi Electricity Company (SEC) and is for different load profiles, such as residential, governmental, commercial, agriculture and industrial. The developed framework receives raw data from SM, preprocess it and then performs the required analysis using the applications layer. Benefits of such a framework are many: standardized data streamlining, unified different data spectrum and at the end create a trustworthy and validated real-based data that can be used to execute many of smart grid applications. This paper describes the structure of the framework, the function of each component and then presents results of several applications to test the validity and performance of the framework. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electrical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00202-020-00949-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1241 Subjects: – SubjectFull: Smart meters Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Load forecasting (Electric power systems) Type: general – SubjectFull: Visual analytics Type: general – SubjectFull: Structural frames Type: general – SubjectFull: Test validity Type: general Titles: – TitleFull: Analytics framework for optimal smart meters data processing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alquthami, Thamer – PersonEntity: Name: NameFull: AlAmoudi, Ahmed – PersonEntity: Name: NameFull: Alsubaie, Abdullah M. – PersonEntity: Name: NameFull: Jaber, Abdulrahman Bin – PersonEntity: Name: NameFull: Alshlwan, Nassir – PersonEntity: Name: NameFull: Anwar, Murad – PersonEntity: Name: NameFull: Al Husaien, Shafi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 09487921 Numbering: – Type: volume Value: 102 – Type: issue Value: 3 Titles: – TitleFull: Electrical Engineering Type: main |
| ResultId | 1 |