Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers
The identification and segmentation of responsive electricity customers have been formulated here as a binary time series clustering (TSC) problem. The assumption of a stationary environment in kernel methods can complicate the mapping of non-stationary time series data to a high-dimensional feature...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11045895/ |
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| author | Amirhossein Ahmadi Hamidreza Zareipour Henry Leung |
| author_facet | Amirhossein Ahmadi Hamidreza Zareipour Henry Leung |
| author_sort | Amirhossein Ahmadi |
| collection | DOAJ |
| description | The identification and segmentation of responsive electricity customers have been formulated here as a binary time series clustering (TSC) problem. The assumption of a stationary environment in kernel methods can complicate the mapping of non-stationary time series data to a high-dimensional feature space, leading to a degradation in the performance of kernel K-means clustering. Hence, a similarity-based non-linear TSC is proposed to capture consumers’ reactions to demand response (DR) signals. K-means with Dynamic Time Warping (DTW) is employed as a nonlinear TSC approach, and to extend it beyond standard sample-to-centroid comparisons, a similarity matrix is suggested in place of raw time series, enabling sample-to-sample comparisons. It is based on distance and correlation matrices ensembling one-to-many and two-to-two comparisons to map original data to the similarity space. By analyzing consumption data from the Low Carbon London project, we demonstrate the effectiveness of our approach in identifying responsive consumers with different responsive levels. |
| format | Article |
| id | doaj-art-07af7eeac60b49ef83e919cc87014617 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-07af7eeac60b49ef83e919cc870146172025-08-20T03:32:42ZengIEEEIEEE Access2169-35362025-01-011310749910751110.1109/ACCESS.2025.358220111045895Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity CustomersAmirhossein Ahmadi0https://orcid.org/0000-0003-3019-8962Hamidreza Zareipour1https://orcid.org/0000-0003-2889-0386Henry Leung2https://orcid.org/0000-0002-5984-107XDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB, CanadaThe identification and segmentation of responsive electricity customers have been formulated here as a binary time series clustering (TSC) problem. The assumption of a stationary environment in kernel methods can complicate the mapping of non-stationary time series data to a high-dimensional feature space, leading to a degradation in the performance of kernel K-means clustering. Hence, a similarity-based non-linear TSC is proposed to capture consumers’ reactions to demand response (DR) signals. K-means with Dynamic Time Warping (DTW) is employed as a nonlinear TSC approach, and to extend it beyond standard sample-to-centroid comparisons, a similarity matrix is suggested in place of raw time series, enabling sample-to-sample comparisons. It is based on distance and correlation matrices ensembling one-to-many and two-to-two comparisons to map original data to the similarity space. By analyzing consumption data from the Low Carbon London project, we demonstrate the effectiveness of our approach in identifying responsive consumers with different responsive levels.https://ieeexplore.ieee.org/document/11045895/Identificationsegmentationresponsive loadstime series clusteringdemand response |
| spellingShingle | Amirhossein Ahmadi Hamidreza Zareipour Henry Leung Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers IEEE Access Identification segmentation responsive loads time series clustering demand response |
| title | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers |
| title_full | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers |
| title_fullStr | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers |
| title_full_unstemmed | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers |
| title_short | Similarity-Based Clustering for Identification and Segmentation of Responsive Electricity Customers |
| title_sort | similarity based clustering for identification and segmentation of responsive electricity customers |
| topic | Identification segmentation responsive loads time series clustering demand response |
| url | https://ieeexplore.ieee.org/document/11045895/ |
| work_keys_str_mv | AT amirhosseinahmadi similaritybasedclusteringforidentificationandsegmentationofresponsiveelectricitycustomers AT hamidrezazareipour similaritybasedclusteringforidentificationandsegmentationofresponsiveelectricitycustomers AT henryleung similaritybasedclusteringforidentificationandsegmentationofresponsiveelectricitycustomers |