Efficient inference in dual-emission FHMM for energy disaggregation

Henning Lange, Mario Berges

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

8 Citations (Scopus)


In this paper an extension to factorial hidden Semi Markov Models is introduced that allows modeling more than one sequence of emissions of the individual HMM chains, as well as a joint emission of all chains. Since exact inference in factorial hidden Markov Models is computationally intractable, an approximate inference technique is introduced that reduces the computational costs by first constraining the successor state space of the model, allowing state changes at statistically significant points in time (events) and by discarding low probability paths (truncating). Furthermore, by being agnostic about state durations the computational costs are further decreased. These assumptions allow for efficient inference that is less susceptible to local minima and allows one to specify the computational burden a priori. The performance of the inference technique is evaluated empirically on a synthetic data set whereas incorporating the feature emissions is evaluated on real world data in the context of energy disaggregation. Energy disaggregation tackles the problem of decomposing whole home energy measurements into the power traces of constituent appliances, and is a natural application for this type of models.

Original languageEnglish
Title of host publicationAAAI Workshop
Subtitle of host publicationTechnical Report Volume WS-16-01 - WS-16-15, 2016
Number of pages7
VolumeWS-16-01 - WS-16-15
ISBN (Electronic)9781577357599
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventAAAI Conference on Artificial Intelligence - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016
Conference number: 30


ConferenceAAAI Conference on Artificial Intelligence
Abbreviated titleAAAI
CountryUnited States


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