Data Imputation Techniques Applied to the Smart Grids Environment
The electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10081311/ |
| _version_ | 1797852324502175744 |
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| author | Jonas Fernando Schreiber Airam Sausen Mauricio De Campos Paulo Sergio Sausen Marco Thome Da Silva Ferreira Filho |
| author_facet | Jonas Fernando Schreiber Airam Sausen Mauricio De Campos Paulo Sergio Sausen Marco Thome Da Silva Ferreira Filho |
| author_sort | Jonas Fernando Schreiber |
| collection | DOAJ |
| description | The electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the amount of errors and missing data, thus hindering the analysis. In this context, this paper performs the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima. The aim is to verify if they could be applied to the electric segment - more specifically to the Smart Grids environment. The database used in the research is obtained from the electricity utility CEEE and its underground substations, located in southern Brazil. Following this, five simulation scenarios are created and one data set is removed, based on pre-established criteria. Finally, the techniques are applied and the new database is compared with the original one. From the simulation results, the technique which presented the best results is Makima, it is validated as robust to be applied in the Smart Grids environment, especially in electrical data missing from an electric power substation. |
| first_indexed | 2024-04-09T19:31:09Z |
| format | Article |
| id | doaj.art-91ada0bd730b4e4fa0ebf7988acc6aa5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T19:31:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91ada0bd730b4e4fa0ebf7988acc6aa52023-04-04T23:00:39ZengIEEEIEEE Access2169-35362023-01-0111319313194010.1109/ACCESS.2023.326218810081311Data Imputation Techniques Applied to the Smart Grids EnvironmentJonas Fernando Schreiber0https://orcid.org/0009-0007-0145-146XAiram Sausen1https://orcid.org/0000-0001-6499-4145Mauricio De Campos2https://orcid.org/0000-0001-8691-2913Paulo Sergio Sausen3https://orcid.org/0000-0001-9863-8800Marco Thome Da Silva Ferreira Filho4Department of Exact Sciences and Engineering, Regional University of Northwestern Rio Grande do Sul (UNIJUÍ), Ijui, BrazilDepartment of Exact Sciences and Engineering, Regional University of Northwestern Rio Grande do Sul (UNIJUÍ), Ijui, BrazilDepartment of Exact Sciences and Engineering, Regional University of Northwestern Rio Grande do Sul (UNIJUÍ), Ijui, BrazilDepartment of Exact Sciences and Engineering, Regional University of Northwestern Rio Grande do Sul (UNIJUÍ), Ijui, BrazilDepartment of Underground Networks, State Electric Power Distribution Company (CEEE-D), Porto Alegre, BrazilThe electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the amount of errors and missing data, thus hindering the analysis. In this context, this paper performs the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima. The aim is to verify if they could be applied to the electric segment - more specifically to the Smart Grids environment. The database used in the research is obtained from the electricity utility CEEE and its underground substations, located in southern Brazil. Following this, five simulation scenarios are created and one data set is removed, based on pre-established criteria. Finally, the techniques are applied and the new database is compared with the original one. From the simulation results, the technique which presented the best results is Makima, it is validated as robust to be applied in the Smart Grids environment, especially in electrical data missing from an electric power substation.https://ieeexplore.ieee.org/document/10081311/Electric power systemsmart gridbig datadata imputation |
| spellingShingle | Jonas Fernando Schreiber Airam Sausen Mauricio De Campos Paulo Sergio Sausen Marco Thome Da Silva Ferreira Filho Data Imputation Techniques Applied to the Smart Grids Environment IEEE Access Electric power system smart grid big data data imputation |
| title | Data Imputation Techniques Applied to the Smart Grids Environment |
| title_full | Data Imputation Techniques Applied to the Smart Grids Environment |
| title_fullStr | Data Imputation Techniques Applied to the Smart Grids Environment |
| title_full_unstemmed | Data Imputation Techniques Applied to the Smart Grids Environment |
| title_short | Data Imputation Techniques Applied to the Smart Grids Environment |
| title_sort | data imputation techniques applied to the smart grids environment |
| topic | Electric power system smart grid big data data imputation |
| url | https://ieeexplore.ieee.org/document/10081311/ |
| work_keys_str_mv | AT jonasfernandoschreiber dataimputationtechniquesappliedtothesmartgridsenvironment AT airamsausen dataimputationtechniquesappliedtothesmartgridsenvironment AT mauriciodecampos dataimputationtechniquesappliedtothesmartgridsenvironment AT paulosergiosausen dataimputationtechniquesappliedtothesmartgridsenvironment AT marcothomedasilvaferreirafilho dataimputationtechniquesappliedtothesmartgridsenvironment |