Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation

Abstract

Non-intrusive load monitoring (NILM) is the prevailing method used to monitor the energy profile of a domestic building and disaggregate the total power consumption into consumption signals by appliance. Whilst the most popular disaggregation algorithms are based on Hidden Markov Model solutions based on deep neural networks have attracted interest from researchers. The objective of this paper is to provide a comprehensive overview of the NILM method and present a comparative review of modern approaches. In this effort, many obstacles are identified. The plethora of metrics, the variety of datasets and the diversity of methodologies make an objective comparison almost impossible. An extensive analysis is made in order to scrutinize these problems. Possible solutions and improvements are suggested, while future research directions are discussed.

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Notes

  1. 1.

    The authors would like to thank Odyssefs Krystalakos for his contribution in developing the software and running the experiments for the appliance set complexity. The source code is available at: https://github.com/christofernal/power-disaggregation-complexity.

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Correspondence to Christoforos Nalmpantis.

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Nalmpantis, C., Vrakas, D. Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif Intell Rev 52, 217–243 (2019). https://doi.org/10.1007/s10462-018-9613-7

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Keywords

  • Non-intrusive load monitoring (NILM)
  • Power disaggregation algorithms
  • Hidden Markov Model
  • Deep learning