skip to main content
10.1145/2487166.2487201acmconferencesArticle/Chapter ViewAbstractPublication Pagese-energyConference Proceedingsconference-collections
poster

Using clustering mechanisms for defining consumer energy services

Published: 21 May 2013 Publication History

Abstract

The ongoing smart grid transformation in utility networks is making available fine grained measurements of electricity consumption. To realize the full potential of the collected data we apply sophisticated data analytics and machine learning techniques to correlate consumption with other types of demographic data (household surveys and tax records) to place the collected consumption data within the right context. This context setting is achieved by a rigorous feature selection procedure, followed by clustering to group customers into peer groups. The statistical information gleaned from these peer groups are then used to identify outliers and define new services both for the utility (energy audits) and the end consumer (Home Energy Health Management systems). Analysis shows that outlier detection within clusters is better able to target customers than outlier detection without clustering: on average, half the outliers found in the clusters would not be outliers in the overall population. The techniques employed could also be used to detect anomalous usage patterns that may be indicative of fraudulent use of electricity.

References

[1]
Kevin Bengtson, "Can Better Utility Bills Save Energy?" Home Energy Magazine Online, May/June 1997
[2]
Chicco, G., Napoli, R., and Piglione F., "Comparisons among clustering techniques for electricity customer classification", IEEE Transactions on Power Systems, vol.21, no.2, May 2006, pp. 933--940.
[3]
Chow, C.K., and Liu, C.N., "Approximating discrete probability distributions with dependence trees", IEEE Transactions on Information Theory 14, no 3 (1968): 462--467.
[4]
Iyer, M., Kempton, W., and Payne, C., "Comparison groups on bills: Automated, personalized energy information", Elsevier B.V., Energy and Buildings 38 (2006) 988--996, www.sciencedirect.com
[5]
Mann-Whitney-Wilcoxon test, http://en.wikipedia.org/wiki/Mann-Whitney_U
[6]
Rasanen, T., Ruuskanen, J., and Kolehmainen, M., "Reducing energy consumption by using self-organizing maps to create more personalized electricity use information", Elsevier, Applied Energy 85 (9), pp. 830--840, Sept 2008.

Cited By

View all
  • (2013)Smart Grid Data ManagementCommunication Networks for Smart Grids10.1007/978-1-4471-6302-2_10(265-284)Online publication date: 20-Dec-2013

Index Terms

  1. Using clustering mechanisms for defining consumer energy services

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    e-Energy '13: Proceedings of the fourth international conference on Future energy systems
    January 2013
    306 pages
    ISBN:9781450320528
    DOI:10.1145/2487166

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 May 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. clustering
    2. energy services
    3. feature selection
    4. smart grid

    Qualifiers

    • Poster

    Conference

    e-Energy '13
    Sponsor:

    Acceptance Rates

    e-Energy '13 Paper Acceptance Rate 40 of 76 submissions, 53%;
    Overall Acceptance Rate 160 of 446 submissions, 36%

    Upcoming Conference

    E-Energy '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 27 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2013)Smart Grid Data ManagementCommunication Networks for Smart Grids10.1007/978-1-4471-6302-2_10(265-284)Online publication date: 20-Dec-2013

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media