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Editorial
. 2022 Sep 3;22(17):6675.
doi: 10.3390/s22176675.

Non-Intrusive Load Monitoring

Affiliations
Editorial

Non-Intrusive Load Monitoring

Luigi Fortuna et al. Sensors (Basel). .

Abstract

Non-Intrusive load monitoring (NILM) represents an emerging strategy based on the application of sevaral multidisciplinary topics [...].

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Conflict of interest statement

The authors declare no conflict of interest.

References

    1. Kaselimi M., Protopapadakis E., Voulodimos A., Doulamis N., Doulamis A. Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring. Sensors. 2022;22:5872. doi: 10.3390/s22155872. - DOI - PMC - PubMed
    1. Zhou M., Shao S., Wang X., Zhu Z., Hu F. Deep Learning-Based Non-Intrusive Commercial Load Monitoring. Sensors. 2022;22:5250. doi: 10.3390/s22145250. - DOI - PMC - PubMed
    1. Hur C.H., Lee H.E., Kim Y.J., Kang S.G. Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring. Sensors. 2022;22:5838. doi: 10.3390/s22155838. - DOI - PMC - PubMed
    1. Massidda L., Marrocu M. A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation. Sensors. 2022;22:4481. doi: 10.3390/s22124481. - DOI - PMC - PubMed
    1. Liu Y., Wang Y., Hong Y., Shi Q., Gao S., Huang X. Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method. Sensors. 2021;21:7272. doi: 10.3390/s21217272. - DOI - PMC - PubMed

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