EconPapers    
Economics at your fingertips  
 

Clustering of residential electricity customers using load time series

Omid Motlagh, Adam Berry and Lachlan O'Neil

Applied Energy, 2019, vol. 237, issue C, 11-24

Abstract: Clustering of electricity customers supports effective market segmentation and management. The literature suggests the clustering of residential customers by their load characteristics. The key challenge is the application of appropriate processes to reduce the extreme dimensionality of load time series to facilitate unique clusters. Time feature extraction is a potential remedy, however, it is limited by the type of noisy, patchy, and unequal time-series common in residential datasets. In this paper we propose a strategy to alleviate these limitations by converting any types of load time series into map models that can be readily clustered. This also results in higher cluster distinction and robustness against noise compared to a baseline feature-based approach. A large dataset of residential electricity customers is used to confirm the outcomes as measured by a number of analytical and industrial metrics. The experiment with 12 clusters results in around 61% distinction, improved coincidence factor by around 6.75% relative to a random grouping, and robustness of around 59% against the applied noise.

Keywords: Electricity consumption; Time series clustering; Unequal time series; Load profile (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918318816
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:237:y:2019:i:c:p:11-24

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-05-11
Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:11-24