Influence of Load Profile Perturbation and Temporal Aggregation on Disaggregation Quality: Unterschied zwischen den Versionen

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|betreuer=Christine Tex
|betreuer=Christine Tex
|termin=Institutsseminar/2018-04-13
|termin=Institutsseminar/2018-04-13
|kurzfassung=TBA
|kurzfassung=Smart Meters become more and more popular. With Smart Meter, new privacy issues arise. A prominent privacy issue is disaggregation, i.e., the determination of appliance usages from aggregated Smart Meter data. The goal of this thesis is to evaluate load profile perturbation and temporal aggregation techniques regarding their ability to prevent disaggregation. To this end, we used a privacy operator framework for temporal aggregation and perturbation, and the NILM TK framework for disaggregation. We evaluated the influence on disaggregation quality of the operators from the framework individually and in combination. One main observation is that the de-noising operator from the framework prevents disaggregation best.
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Aktuelle Version vom 10. April 2018, 16:30 Uhr

Vortragende(r) Robin Miller
Vortragstyp Bachelorarbeit
Betreuer(in) Christine Tex
Termin Fr 13. April 2018
Vortragsmodus
Kurzfassung Smart Meters become more and more popular. With Smart Meter, new privacy issues arise. A prominent privacy issue is disaggregation, i.e., the determination of appliance usages from aggregated Smart Meter data. The goal of this thesis is to evaluate load profile perturbation and temporal aggregation techniques regarding their ability to prevent disaggregation. To this end, we used a privacy operator framework for temporal aggregation and perturbation, and the NILM TK framework for disaggregation. We evaluated the influence on disaggregation quality of the operators from the framework individually and in combination. One main observation is that the de-noising operator from the framework prevents disaggregation best.