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Anomaly Detection in Electricity Consumption Data
Halmstad University, School of Information Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Distribution grids play an important role in delivering electricityto end users. Electricity customers would like to have a continuouselectricity supply without any disturbance. For customerssuch as airports and hospitals electricity interruption may havedevastating consequences. Therefore, many electricity distributioncompanies are looking for ways to prevent power outages.Sometimes the power outages are caused from the grid sidesuch as failure in transformers or a break down in power cablesbecause of wind. And sometimes the outages are caused bythe customers such as overload. In fact, a very high peak inelectricity consumption and irregular load profile may causethese kinds of failures.In this thesis, we used an approach consisting of two mainsteps for detecting customers with irregular load profile. In thefirst step, we create a dictionary based on all common load profileshapes using daily electricity consumption for one-monthperiod. In the second step, the load profile shapes of customersfor a specific week are compared with the load patterns in thedictionary. If the electricity consumption for any customer duringthat week is not similar to any of the load patterns in thedictionary, it will be grouped as an anomaly. In this case, loadprofile data are transformed to symbols using Symbolic AggregateapproXimation (SAX) and then clustered using hierarchicalclustering.The approach is used to detect anomaly in weekly load profileof a data set provided by HEM Nät, a power distributioncompany located in the south of Sweden.

Place, publisher, year, edition, pages
2017. , p. 68
Keywords [en]
electricity consumption, smart meter data, symbolic representation, anomaly detection
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-35011OAI: oai:DiVA.org:hh-35011DiVA, id: diva2:1142213
Subject / course
Information Technology
Educational program
Master's Programme in Information Technology, 120 credits
Presentation
2017-06-02, D315, Halmstad University, Halmstad, 09:30 (English)
Supervisors
Examiners
Available from: 2017-09-19 Created: 2017-09-18 Last updated: 2017-09-19Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf