Unsupervised machine learning techniques for energy consumption tariff design (pending publication)

Authors:

  • Tsakiris Georgios, Virtsionis-Gkalinikis Nikolaos, Mischos Stavros, Vrakas Dimitrios

Availiability:

Appeared In:

  • Unsupervised machine learning techniques for energy consumption tariff design

Keywords:

  • Time-Of-Use pricing, residential energy consumption, fuzzy clus- tering, unsupervised machine learning

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Abstract:

  • Dealing with electricity demand fluctuations throughout peak and off-peak periods is challenging for electricity companies. During peak demand times, the grid should be able to match the high consumer needs. Conversely, minimal usage during off-peak periods leads to underutilization of generation capacity. This imbalance challenges utilities to ensure sufficient capacity and devise fair pric- ing models. The Time-of-Use (ToU) pricing model has emerged as a viable solution in many countries, encouraging consumers to shift their energy consumption from expensive peak hours to more affordable off-peak periods. To this end, this paper proposes unsupervised machine learning methods for designing ToU tariffs using only energy consumption time series data. Additionally, a new metric is introduced to evaluate the adaptability of the ToU methods to fluctuations in energy consumption. To validate the implemented techniques, public datasets from different countries were used.
Address

School of Informatics
Aristotle University of Thessaloniki
54124 Thessaloniki, Greece

Contact

Prof. Ioannis P. Vlahavas
tel: ++30 231099-8145
fax: ++30 231099-8362
e-mail: vlahavas@csd.auth.gr

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