Event Detection for Energy Consumption Monitoring

Daniel Jorde, Hans Arno Jacobsen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The accurate detection of appliance state transitions in electrical signals is fundamental for numerous energy-conserving applications. We present an extensive overview and categorization of the current state in event detection on high-sampling-rate signals. Existing approaches are designed for specific environments and need to be tediously adapted for new ones. Thus, we propose an unsupervised, multi-environment event detector, outperforming four state-of-the-art algorithms on two heterogeneous public datasets.

Original languageEnglish
Pages (from-to)703-709
Number of pages7
JournalIEEE Transactions on Sustainable Computing
Volume6
Issue number4
DOIs
StatePublished - 2021

Keywords

  • Energy-aware systems
  • Event detection
  • Machine learning
  • Neural nets
  • Non-intrusive load monitoring

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