Energy disaggregation aims at reconstructing the power consumed by each electric appliance available in a household from the aggregate power readings collected by a single-point smart meter. With the ultimate goal of fully automatizing this procedure, we first estimate a set of jump models, each of them describing the consumption behaviour of each electric appliance. By representing the total power consumed at the household level as the sum of the outputs of the estimated jump models, a filtering algorithm, based on dynamic programming, is then employed to reconstruct, in an iterative way, the power consumption at an individual appliance level.

Jump model learning and filtering for energy end-use disaggregation

Breschi V.;
2018

Abstract

Energy disaggregation aims at reconstructing the power consumed by each electric appliance available in a household from the aggregate power readings collected by a single-point smart meter. With the ultimate goal of fully automatizing this procedure, we first estimate a set of jump models, each of them describing the consumption behaviour of each electric appliance. By representing the total power consumed at the household level as the sum of the outputs of the estimated jump models, a filtering algorithm, based on dynamic programming, is then employed to reconstruct, in an iterative way, the power consumption at an individual appliance level.
Proceedings of the 18th IFAC Symposium on System Identification SYSID 2018
Energy disaggregation
Filtering
Jump models
Non-intrusive appliance load monitoring
Recursive estimate
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167017
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact