The role of smart electricity meter data analysis in driving sustainable development
The analysis of Smart Electricity Meter (SEM) data, which plays an important role in sustainability of the electricity system. The widespread use SEM generates a substantial volume of data. However, when faced with an influx of new data, traditional clustering methods require re-clustering all the d...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000445 |
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| author | Archana Y. Chaudhari Preeti Mulay Shradha Chavan |
| author_facet | Archana Y. Chaudhari Preeti Mulay Shradha Chavan |
| author_sort | Archana Y. Chaudhari |
| collection | DOAJ |
| description | The analysis of Smart Electricity Meter (SEM) data, which plays an important role in sustainability of the electricity system. The widespread use SEM generates a substantial volume of data. However, when faced with an influx of new data, traditional clustering methods require re-clustering all the data from scratch. To address the challenge of handling the ever-increasing data, an incremental clustering algorithm proves to be the most suitable choice. Proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm updates load patterns without relying on overall daily load curve clustering. The CGMIC algorithm first extracts load patterns from new data and then either intergrades the existing load patterns or forms new ones. The IITB Indian Residential Energy Dataset,is utilized to validate the proposed system. The performance of CGMIC compared with DBSCAN on silhouette score and Davis Bouldin index metrics. The insight of this research contributes directly to sustainable development goals. By effectively identifies changes in residential electricity consumption behavior. • The proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm, updating load patterns incrementally, avoiding the need to re-cluster all data from scratch. • The CGMIC algorithm is validated using IITB Indian Residential Energy Dataset. Effectiveness is measured using metrics like the silhouette score and Davis Bouldin index. • The insights from the CGMIC algorithm help identify changes in residential electricity consumption behavior, providing valuable information for utility companies to optimize electricity load management, thereby contributing to sustainable development goals. |
| format | Article |
| id | doaj-art-66fe254c16974e179c9840bab1800742 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-66fe254c16974e179c9840bab18007422025-02-08T05:00:31ZengElsevierMethodsX2215-01612025-06-0114103196The role of smart electricity meter data analysis in driving sustainable developmentArchana Y. Chaudhari0Preeti Mulay1Shradha Chavan2Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India; Corresponding author.Founder, Weekend Forever, Pune, IndiaSchool of CSIT, Symbiosis Skills and Professional University, Kiwale, Pune, Maharashtra, IndiaThe analysis of Smart Electricity Meter (SEM) data, which plays an important role in sustainability of the electricity system. The widespread use SEM generates a substantial volume of data. However, when faced with an influx of new data, traditional clustering methods require re-clustering all the data from scratch. To address the challenge of handling the ever-increasing data, an incremental clustering algorithm proves to be the most suitable choice. Proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm updates load patterns without relying on overall daily load curve clustering. The CGMIC algorithm first extracts load patterns from new data and then either intergrades the existing load patterns or forms new ones. The IITB Indian Residential Energy Dataset,is utilized to validate the proposed system. The performance of CGMIC compared with DBSCAN on silhouette score and Davis Bouldin index metrics. The insight of this research contributes directly to sustainable development goals. By effectively identifies changes in residential electricity consumption behavior. • The proposed Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm, updating load patterns incrementally, avoiding the need to re-cluster all data from scratch. • The CGMIC algorithm is validated using IITB Indian Residential Energy Dataset. Effectiveness is measured using metrics like the silhouette score and Davis Bouldin index. • The insights from the CGMIC algorithm help identify changes in residential electricity consumption behavior, providing valuable information for utility companies to optimize electricity load management, thereby contributing to sustainable development goals.http://www.sciencedirect.com/science/article/pii/S2215016125000445Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm |
| spellingShingle | Archana Y. Chaudhari Preeti Mulay Shradha Chavan The role of smart electricity meter data analysis in driving sustainable development MethodsX Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm |
| title | The role of smart electricity meter data analysis in driving sustainable development |
| title_full | The role of smart electricity meter data analysis in driving sustainable development |
| title_fullStr | The role of smart electricity meter data analysis in driving sustainable development |
| title_full_unstemmed | The role of smart electricity meter data analysis in driving sustainable development |
| title_short | The role of smart electricity meter data analysis in driving sustainable development |
| title_sort | role of smart electricity meter data analysis in driving sustainable development |
| topic | Closeness-based Gaussian Mixture Incremental Clustering (CGMIC) Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000445 |
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