Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing

Derck Koolen, Navid Sadat-Razavi, Wolfgang Ketter*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer's personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.

Original languageEnglish
Article number1160
JournalApplied Sciences (Switzerland)
Issue number11
Publication statusPublished - 12 Nov 2017
Externally publishedYes


  • Customer segmentation
  • Demand response
  • Demand side management
  • Dynamic pricing
  • Home energy management system
  • Machine learning
  • Recommendation systems


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