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Electricity Consumption Time Series Profiling: A Data Mining Application in Energy Industry

Hongyan Liu, Zhiyuan Yao, Tomas Eklund, Barbro Back, Electricity Consumption Time Series Profiling: A Data Mining Application in Energy Industry. In: Petra Perner (Ed.), Advances in Data Mining: Applications and Theoretical Aspects, Lecture Notes in Computer Science 7377, 52–66, Springer, 2012.

http://dx.doi.org/10.1007/978-3-642-31488-9_5

Abstract:

The ongoing deployment of Automated Meter Reading systems (AMR) in the European electricity industry has created new challenges for electricity utilities in terms of how to fully utilise the wealth of timely measured AMR data, not only to enhance day-to-day operations, but also to facilitate demand response programs. In this study we investigate a visual data mining approach for decision-making support with respect to pricing differentiation or designing demand response tariffs. 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 visualise deviations and granular information in consumption patterns, allowing the electricity companies to gain better knowledge about the
customers’ electricity usage. The investigated electricity consumption time series profiling approach will add empirical understanding of the problem domain to the related research community and to the future practice of the
energy industry.

BibTeX entry:

@INPROCEEDINGS{inpLiYaEkBa12b,
  title = {Electricity Consumption Time Series Profiling: A Data Mining Application in Energy Industry},
  booktitle = {Advances in Data Mining: Applications and Theoretical Aspects},
  author = {Liu, Hongyan and Yao, Zhiyuan and Eklund, Tomas and Back, Barbro},
  volume = {7377},
  series = {Lecture Notes in Computer Science},
  editor = {Perner, Petra},
  publisher = {Springer},
  pages = {52–66},
  year = {2012},
  keywords = {Visual Data Mining, Clustering, Business Intelligence, Electricity Consumption Profiling, Self-Organizing Maps, Deviation Detection},
}

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

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