Where academic tradition
meets the exciting future

You are here: TUCS > PUBLICATIONS > Publication Search > Electricity Consumption Time S...

Electricity Consumption Time Series Profiling: A Business Intelligence Case

Hongyan Liu, Zhiyuan Yao, Tomas Eklund, Barbro Back, Electricity Consumption Time Series Profiling: A Business Intelligence Case. TUCS Technical Reports 1031, TUCS, 2011.

Abstract:

The ongoing deployment of AMR (Automated Meter Reading) in the European electricity industry has introduced new challenges for companies in terms of how to fully utilise the timely measured AMR data, not only to enhance day-to-day operations, but also to facilitate demand response. We examine a business intelligence approach based on visual data mining techniques in the form of Self-Organising Maps. We cluster the customers in our sample according to the customers’ actual consumption behaviour in 2009, and profile their electricity consumption with a focus on the comparison of two sets of seasonal and time based variables. The results suggest that such an analytical approach can discover hidden consumption patterns, allowing the electricity companies to gain better knowledge about their customers’ electricity usage. Additionally, we propose four time bands which can reveal more detailed information for the company to take into account regarding pricing differentiation or designing demand response tariffs.

Files:

Full publication in PDF-format

BibTeX entry:

@TECHREPORT{tLiYaEkBa11b,
  title = {Electricity Consumption Time Series Profiling: A Business Intelligence Case},
  author = {Liu, Hongyan and Yao, Zhiyuan and Eklund, Tomas and Back, Barbro},
  number = {1031},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2011},
  keywords = {Electricity Consumption Profiling, Business Intelligence, Visual Data Mining, Self-Organizing Maps, and Electricity Distribution},
  ISBN = {978-952-12-2689-2},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

Edit publication