main.bbl

\begin{thebibliography}{-------}
\providecommand{\natexlab}[1]{#1}

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Hart, G.W.
\newblock Prototype Nonintrusive Appliance Load Monitor.
\newblock Technical Report~2, {MIT Energy Laboratory and Electric Power
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\bibitem[Hart(1992)]{hart1992}
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\bibitem[Zeifman and Roth(2011)]{zeifman2011}
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\newblock Nonintrusive Appliance Load Monitoring: {{Review}} and Outlook.
\newblock {\em IEEE Transactions on Consumer Electronics} {\bf 2011}, {\em
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\bibitem[Alcal{\'a}(2016)]{alcala2016}
Alcal{\'a}, J.
\newblock Non-{{Intrusive Load Monitoring}} Techniques for {{Activity}} of
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\newblock PhD thesis, Universidad de Alcal\'a, {Madrid},  2016.

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\newblock Non Intrusive Load Monitoring for Demand Side Management.
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{\c C}imen, H.; {\c C}etinkaya, N.; Vasquez, J.C.; Guerrero, J.M.
\newblock A {{Microgrid Energy Management System}} Based on {{Non}}-{{Intrusive
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\newblock {\em IEEE Transactions on Smart Grid} {\bf 2020}, pp. 1--1.
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\newblock {{ElectriSense}}: {{Single}}-Point {{Sensing Using EMI}} for
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\newblock State of the {{Art}} and {{Trends Review}} of {{Smart Metering}} in
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\newblock {\em Applied Sciences} {\bf 2016}, {\em 6},~68.
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\bibitem[Kim \em{et~al.}(2011)Kim, Marwah, Arlitt, Lyon, and Han]{kim2011}
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\newblock Unsupervised {{Disaggregation}} of {{Low Frequency Power
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\bibitem[Kolter and Jaakkola(2012)]{kolter2012}
Kolter, J.Z.; Jaakkola, T.S.
\newblock Approximate {{Inference}} in {{Additive Factorial HMMs}} with
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\newblock  {{AISTATS}},  2012, Vol.~22, pp. 1472--1482.

\bibitem[Parson \em{et~al.}(2014)Parson, Ghosh, Weal, and Rogers]{parson2014a}
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\newblock An Unsupervised Training Method for Non-Intrusive Appliance Load
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\newblock {\em Artificial Intelligence} {\bf 2014}, {\em 217},~1--19.
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\bibitem[Makonin \em{et~al.}(2016)Makonin, Popowich, Baji{\'c}, Gill, and
  Bartram]{makonin2016}
Makonin, S.; Popowich, F.; Baji{\'c}, I.V.; Gill, B.; Bartram, L.
\newblock Exploiting {{HMM Sparsity}} to {{Perform Online Real}}-{{Time
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\newblock {\em IEEE Transactions on Smart Grid} {\bf 2016}, {\em
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  doi:{\changeurlcolor{black}\href{https://doi.org/10.1109/TSG.2015.2494592}{\detokenize{10.1109/TSG.2015.2494592}}}.

\bibitem[Tabatabaei \em{et~al.}(2017)Tabatabaei, Dick, and Xu]{tabatabaei2017}
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\newblock Toward {{Non}}-{{Intrusive Load Monitoring}} via {{Multi}}-{{Label
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\newblock {\em IEEE Transactions on Smart Grid} {\bf 2017}, {\em 8},~26--40.
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\bibitem[Kelly and Knottenbelt(2015)]{kelly2015b}
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\newblock Neural {{NILM}}: {{Deep Neural Networks Applied}} to {{Energy
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\bibitem[Mauch and Yang(2015)]{mauch2015}
Mauch, L.; Yang, B.
\newblock A New Approach for Supervised Power Disaggregation by Using a Deep
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\newblock  Signal and {{Information Processing}} ({{GlobalSIP}}), 2015 {{IEEE
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\bibitem[Zoha \em{et~al.}(2012)Zoha, Gluhak, Imran, and Rajasegarar]{zoha2012}
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\newblock Non-Intrusive Load Monitoring Approaches for Disaggregated Energy
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\newblock {\em Sensors} {\bf 2012}, {\em 12},~16838--16866.

\bibitem[Bonfigli \em{et~al.}(2015)Bonfigli, Squartini, Fagiani, and
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Bonfigli, R.; Squartini, S.; Fagiani, M.; Piazza, F.
\newblock Unsupervised Algorithms for Non-Intrusive Load Monitoring: {{An}}
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\newblock {IEEE},  2015, pp. 1175--1180.
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\bibitem[Pereira and Nunes(2018)]{pereira2018}
Pereira, L.; Nunes, N.
\newblock Performance Evaluation in Non-Intrusive Load Monitoring:
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\newblock {\em Wiley Interdisciplinary Reviews: Data Mining and Knowledge
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\bibitem[Nalmpantis and Vrakas(2019)]{nalmpantis2019}
Nalmpantis, C.; Vrakas, D.
\newblock Machine Learning Approaches for Non-Intrusive Load Monitoring: From
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\newblock {\em Artificial Intelligence Review} {\bf 2019}, {\em 52},~217--243.
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\bibitem[Bonfigli and Squartini(2020)]{bonfigli2020}
Bonfigli, R.; Squartini, S.
\newblock {\em Machine {{Learning Approaches}} to {{Non}}-{{Intrusive Load
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\bibitem[Bonfigli \em{et~al.}(2018)Bonfigli, Felicetti, Principi, Fagiani,
  Squartini, and Piazza]{bonfigli2018}
Bonfigli, R.; Felicetti, A.; Principi, E.; Fagiani, M.; Squartini, S.; Piazza,
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\newblock Denoising Autoencoders for {{Non}}-{{Intrusive Load Monitoring}}:
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\newblock {\em Energy and Buildings} {\bf 2018}, {\em 158},~1461--1474.
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\bibitem[Valenti \em{et~al.}(2018)Valenti, Bonfigli, Principi, and
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Valenti, M.; Bonfigli, R.; Principi, E.; Squartini, a.S.
\newblock Exploiting the {{Reactive Power}} in {{Deep Neural Models}} for
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\newblock  2018 {{International Joint Conference}} on {{Neural Networks}}
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\newblock Towards {{Reproducible State}}-of-the-Art {{Energy Disaggregation}}.
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Reinhardt, A.; Klemenjak, C.
\newblock How Does {{Load Disaggregation Performance Depend}} on {{Data
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\newblock  Proceedings of the {{Eleventh ACM International Conference}} on
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\bibitem[Batra \em{et~al.}(2014)Batra, Kelly, Parson, Dutta, Knottenbelt,
  Rogers, Singh, and Srivastava]{batra2014a}
Batra, N.; Kelly, J.; Parson, O.; Dutta, H.; Knottenbelt, W.; Rogers, A.;
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\newblock {{NILMTK}}: An Open Source Toolkit for Non-Intrusive Load Monitoring.
\newblock {ACM Press},  2014, pp. 265--276.
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\newblock {{NILMTK}} v0.2: A Non-Intrusive Load Monitoring Toolkit for Large
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\bibitem[Li and Dick(2016)]{li2016}
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\newblock Whole-House {{Non}}-{{Intrusive Appliance Load Monitoring}} via
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\newblock  2016 {{International Joint Conference}} on {{Neural Networks}}
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\newblock Multi {{Label Restricted Boltzmann Machine}} for {{Non}}-Intrusive
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\newblock  {{ICASSP}} 2019-2019 {{IEEE International Conference}} on
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\newblock  Proceedings of the 7th {{ACM International Conference}} on
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Brewitt, C.; Goddard, N.
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Makonin, S.; Ellert, B.; Baji{\'c}, I.V.; Popowich, F.
\newblock Electricity, Water, and Natural Gas Consumption of a Residential
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\newblock {\em Scientific Data} {\bf 2016}, {\em 3},~160037.
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Makonin, S.
\newblock {{AMPds2}}: {{The Almanac}} of {{Minutely Power}} Dataset
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  Xu, Z.; Yang, Q.; Yu, F.X.; Yu, H.; Zhao, S.
\newblock Advances and {{Open Problems}} in {{Federated Learning}}.
\newblock {\em arXiv:1912.04977 [cs, stat]} {\bf 2019},
  \href{http://xxx.lanl.gov/abs/1912.04977}{{\normalfont [arXiv:cs,
  stat/1912.04977]}}.

\end{thebibliography}