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Title: Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Abstract

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1398467
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: AAAI-17 Workshop on artificial intelligence for smart grids and smart buildings, San Francisco, CA (United States), 4-5 Feb 2017
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Jin, Ling, Lee, Doris, Sim, Alex, Borgeson, Sam, Wu, Kesheng, Spurlock, C. Anna, and Todd, Annika. Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data. United States: N. p., 2017. Web.
Jin, Ling, Lee, Doris, Sim, Alex, Borgeson, Sam, Wu, Kesheng, Spurlock, C. Anna, & Todd, Annika. Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data. United States.
Jin, Ling, Lee, Doris, Sim, Alex, Borgeson, Sam, Wu, Kesheng, Spurlock, C. Anna, and Todd, Annika. Tue . "Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data". United States. doi:. https://www.osti.gov/servlets/purl/1398467.
@article{osti_1398467,
title = {Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data},
author = {Jin, Ling and Lee, Doris and Sim, Alex and Borgeson, Sam and Wu, Kesheng and Spurlock, C. Anna and Todd, Annika},
abstractNote = {Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 21 00:00:00 EDT 2017},
month = {Tue Mar 21 00:00:00 EDT 2017}
}

Conference:
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