Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles
There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular tec...
| Main Authors: | , , |
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| Format: | Journal article |
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Institute of Electrical and Electronics Engineers
2014
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| _version_ | 1797106078310203392 |
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| author | Granell, R Axon, C Wallom, D |
| author_facet | Granell, R Axon, C Wallom, D |
| author_sort | Granell, R |
| collection | OXFORD |
| description | There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security. |
| first_indexed | 2024-03-07T06:56:35Z |
| format | Journal article |
| id | oxford-uuid:fe57b392-8f13-4396-84e5-8f81d80ecdcd |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:56:35Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:fe57b392-8f13-4396-84e5-8f81d80ecdcd2022-03-27T13:35:40ZImpacts of raw data temporal resolution using selected clustering methods on residential electricity load profilesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fe57b392-8f13-4396-84e5-8f81d80ecdcdSymplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2014Granell, RAxon, CWallom, DThere is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security. |
| spellingShingle | Granell, R Axon, C Wallom, D Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title | Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title_full | Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title_fullStr | Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title_full_unstemmed | Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title_short | Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| title_sort | impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles |
| work_keys_str_mv | AT granellr impactsofrawdatatemporalresolutionusingselectedclusteringmethodsonresidentialelectricityloadprofiles AT axonc impactsofrawdatatemporalresolutionusingselectedclusteringmethodsonresidentialelectricityloadprofiles AT wallomd impactsofrawdatatemporalresolutionusingselectedclusteringmethodsonresidentialelectricityloadprofiles |