Analysis and clustering of residential customers energy behavioral demand using smart meter data
Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identificat...
| Main Authors: | , , |
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| Format: | Journal article |
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Institute of Electrical and Electronics Engineers
2015
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| _version_ | 1797056920172888064 |
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| author | Haben, S Singleton, C Grindrod, P |
| author_facet | Haben, S Singleton, C Grindrod, P |
| author_sort | Haben, S |
| collection | OXFORD |
| description | Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and the improvement of energy profile modeling. However, due to the high stochasticity and irregularity of household-level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper, we present in-depth analysis of customer smart meter data to better understand the peak demand and major sources of variability in their behavior. We find four key time periods, in which the data should be analyzed, and use this to form relevant attributes for our clustering. We present a finite mixture model-based clustering, where we discover ten distinct behavior groups describing customers based on their demand and their variability. Finally, using an existing bootstrap technique, we show that the clustering is reliable. To the authors' knowledge, this is the first time in the power systems literature that the sample robustness of the clustering has been tested. |
| first_indexed | 2024-03-06T19:29:16Z |
| format | Journal article |
| id | oxford-uuid:1ce37cae-002f-4620-8acc-d6c8a8dbbdfe |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:29:16Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:1ce37cae-002f-4620-8acc-d6c8a8dbbdfe2022-03-26T11:07:59ZAnalysis and clustering of residential customers energy behavioral demand using smart meter dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1ce37cae-002f-4620-8acc-d6c8a8dbbdfeSymplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2015Haben, SSingleton, CGrindrod, PClustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and the improvement of energy profile modeling. However, due to the high stochasticity and irregularity of household-level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper, we present in-depth analysis of customer smart meter data to better understand the peak demand and major sources of variability in their behavior. We find four key time periods, in which the data should be analyzed, and use this to form relevant attributes for our clustering. We present a finite mixture model-based clustering, where we discover ten distinct behavior groups describing customers based on their demand and their variability. Finally, using an existing bootstrap technique, we show that the clustering is reliable. To the authors' knowledge, this is the first time in the power systems literature that the sample robustness of the clustering has been tested. |
| spellingShingle | Haben, S Singleton, C Grindrod, P Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title | Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title_full | Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title_fullStr | Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title_full_unstemmed | Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title_short | Analysis and clustering of residential customers energy behavioral demand using smart meter data |
| title_sort | analysis and clustering of residential customers energy behavioral demand using smart meter data |
| work_keys_str_mv | AT habens analysisandclusteringofresidentialcustomersenergybehavioraldemandusingsmartmeterdata AT singletonc analysisandclusteringofresidentialcustomersenergybehavioraldemandusingsmartmeterdata AT grindrodp analysisandclusteringofresidentialcustomersenergybehavioraldemandusingsmartmeterdata |