Anomaly detection of building systems using energy demand frequency domain analysis

Michael Wrinch, Tarek H.M. El-Fouly, Steven Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

This paper presents and demonstrates a method to quickly identify when regular periodic activities, such as a daily night setback on a thermostat, are inappropriately configured or accidentally reset. Anomalies in periodic building operations are identified by analyzing smart meter electrical demand data in the frequency domain with a weekly travelling time window instead of using time domain functions such as load factor. Initial experiments on a real site found that spectral energy signals for periodic (frequency) hours of 4, 6, 8, 12 and days 1, 3.5 and 7 to be greatly reduced when a device is not functioning appropriately. In addition, the ratio of the DC offset (0 Hz) energy with the other higher periodic energies can normalize the periodic energies to a relative index that can then be used for comparing other seasons and other buildings for periodical performance.

Original languageBritish English
Title of host publication2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs
StatePublished - 2012
Event2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States
Duration: 22 Jul 201226 Jul 2012

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2012 IEEE Power and Energy Society General Meeting, PES 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period22/07/1226/07/12

Keywords

  • Energy conservation
  • Energy efficiency
  • Energy management
  • Load management
  • Power system measurements
  • Smart grids

Fingerprint

Dive into the research topics of 'Anomaly detection of building systems using energy demand frequency domain analysis'. Together they form a unique fingerprint.

Cite this