Chronological categorization and decomposition of customer loads

Nourbakhsh, Ghavameddin, Eden, Gary, McVeigh, Dylan, & Ghosh, Arindam (2012) Chronological categorization and decomposition of customer loads. IEEE Transactions on Power Delivery, 27(4), pp. 2270-2277.

View at publisher

Abstract

The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.

Impact and interest:

8 citations in Scopus
Search Google Scholar™
5 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

133 since deposited on 10 Feb 2013
11 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 57048
Item Type: Journal Article
Refereed: Yes
Keywords: Load modeling, load distribution, customer loads decomposition, Classification, K-means, clustering, decomposition, load profiling
DOI: 10.1109/TPWRD.2012.2204072
ISSN: 1937-4208 (online) 0885-8977 (print)
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Power and Energy Systems Engineering (excl. Renewable Power) (090607)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Electrical and Electronic Engineering not elsewhere classified (090699)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 IEEE
Deposited On: 10 Feb 2013 23:22
Last Modified: 16 Feb 2013 07:29

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page