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Energy Efficiency

, Volume 11, Issue 4, pp 939–953 | Cite as

Towards novelty detection in electronic devices based on their energy consumption

  • Thamires Campos Luz
  • Fábio L. Verdi
  • Tiago A. Almeida
Original Article

Abstract

Electricity in Brazil is mostly generated by hydroelectric plants that depend on the volume of water in their reservoirs. Due to the fact that rainfall is dramatically decreasing year by year, alternative methods, much more expensive, are often required to supply the energy demand. The increasing number of electronic devices, overconsumption, and energy wasting are also contributing to the problem. There are many ways for wasting energy, often as a result of malfunction devices or human faults. In this way, to assist consumers to save energy and repair a possibly damaged equipment, we propose a system to monitor the energy consumption of electronic devices in order to automatically detect novelties and send alerts. For this, we have evaluated the performance of established machine learning methods, such as Sliding Window, Exponentially Weighted Moving Averages, Clustering, Average consumption by Cycle and Stage, Gauss Distribution, and Artificial Neural Networks. The results show that such methods are very efficient in real-time novelties detection, since they have presented a balanced performance with a high novelty detection rate and low false alarm rate.

Keywords

Novelty detection Fault detection Energy consumption Machine learning 

Notes

Acknowledgements

The authors are grateful for financial support from the Brazilian agencies FAPESP, Capes, and CNPq.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Thamires Campos Luz
    • 1
  • Fábio L. Verdi
    • 1
  • Tiago A. Almeida
    • 1
  1. 1.Department of Computer ScienceFederal University of São Carlos - UFSCarSorocabaBrazil

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