Proceedings of the 2011 SIAM International Conference on Data Mining 2011
DOI: 10.1137/1.9781611972818.64
|View full text |Cite
|
Sign up to set email alerts
|

Abstract: Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accuratel… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
335
0
1

Year Published

2013
2013
2018
2018

Publication Types

Select...
5
2
1

Relationship

0
8

Authors

Journals

citations
Cited by 469 publications
(337 citation statements)
references
References 49 publications
1
335
0
1
Order By: Relevance
“…HMM-based NILM tackles the disaggregation problem through the variants of HMM, which are capable of modeling the additive nature of the total power consumption presented as factorial HMMs (FHMMs) [11,16,21,28,29]. The robustness and applicability of these models in supervised and unsupervised NILM solutions have been largely demonstrated.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…HMM-based NILM tackles the disaggregation problem through the variants of HMM, which are capable of modeling the additive nature of the total power consumption presented as factorial HMMs (FHMMs) [11,16,21,28,29]. The robustness and applicability of these models in supervised and unsupervised NILM solutions have been largely demonstrated.…”
Section: Related Workmentioning
confidence: 99%
“…The proposed MCMC techniques generally used to decode the hidden states are simulated annealing [11], Gibbs sampling [31], and reversible Jump MCMC (RJMCMC) [32]. Theses methods are preferred because of their ability to simplify the setting-up sampling schema.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…These works propose the use of Fourier series, neural networks, spectral envelopes, power changes and other waveform features. There are other works based on hidden Markov models (HMMs) (Elliott et al 1995) for load disaggregation (Kim et al 2011;Kolter 2011). However, HMM does not work well if an individual load changes its load pattern due to DR policies.…”
Section: Load Disaggregationmentioning
confidence: 99%
“…On the other hand, in [11], the authors propose to disaggregate the energy consumption from a single smart meter by using some prior knowledge of appliances with the help of a domain expert. An unsupervised technique to disaggregate energy consumption using factorial hidden markov models (FHMMs) is proposed in [12]. In [13], a Markovian model is proposed for home energy consumption, storage sizing and transformer sizing in the distributed network.…”
Section: Related Workmentioning
confidence: 99%