Electricity Consumption Clustering Using Smart Meter Data
Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/4/859 |
| _version_ | 1798039383211769856 |
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| author | Alexander Tureczek Per Sieverts Nielsen Henrik Madsen |
| author_facet | Alexander Tureczek Per Sieverts Nielsen Henrik Madsen |
| author_sort | Alexander Tureczek |
| collection | DOAJ |
| description | Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method. |
| first_indexed | 2024-04-11T21:53:08Z |
| format | Article |
| id | doaj.art-4464971562484dabbe55bcd65aecc795 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:53:08Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4464971562484dabbe55bcd65aecc7952022-12-22T04:01:11ZengMDPI AGEnergies1996-10732018-04-0111485910.3390/en11040859en11040859Electricity Consumption Clustering Using Smart Meter DataAlexander Tureczek0Per Sieverts Nielsen1Henrik Madsen2Systems Analysis, the Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, DenmarkSystems Analysis, the Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, DenmarkDynamical Systems, the Department Compute, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, DenmarkElectricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method.http://www.mdpi.com/1996-1073/11/4/859smart meter analysiselectricity consumption clusteringdata analysisK-Meansautocorrelation |
| spellingShingle | Alexander Tureczek Per Sieverts Nielsen Henrik Madsen Electricity Consumption Clustering Using Smart Meter Data Energies smart meter analysis electricity consumption clustering data analysis K-Means autocorrelation |
| title | Electricity Consumption Clustering Using Smart Meter Data |
| title_full | Electricity Consumption Clustering Using Smart Meter Data |
| title_fullStr | Electricity Consumption Clustering Using Smart Meter Data |
| title_full_unstemmed | Electricity Consumption Clustering Using Smart Meter Data |
| title_short | Electricity Consumption Clustering Using Smart Meter Data |
| title_sort | electricity consumption clustering using smart meter data |
| topic | smart meter analysis electricity consumption clustering data analysis K-Means autocorrelation |
| url | http://www.mdpi.com/1996-1073/11/4/859 |
| work_keys_str_mv | AT alexandertureczek electricityconsumptionclusteringusingsmartmeterdata AT persievertsnielsen electricityconsumptionclusteringusingsmartmeterdata AT henrikmadsen electricityconsumptionclusteringusingsmartmeterdata |