English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To reduce energy demand in households it is useful to know which electricalappliances are in use at what times. Monitoring individual appliances is costlyand intrusive, whereas data on overall household electricity use is more easilyobtained. In this paper, we consider the energy disaggregation problem where ahousehold's electricity consumption is disaggregated into the componentappliances. The factorial hidden Markov model (FHMM) is a natural model to fitthis data. We enhance this generic model by introducing two constraints on thestate sequence of the FHMM. The first is to use a non-homogeneous Markov chain,modelling how appliance usage varies over the day, and the other is to enforcethat at most one chain changes state at each time step. This yields a new modelwhich we call the interleaved factorial non-homogeneous hidden Markov model(IFNHMM). We evaluated the ability of this model to perform disaggregation inan ultra-low frequency setting, over a data set of 251 English households. Inthis new setting, the IFNHMM outperforms the FHMM in terms of recovering theenergy used by the component appliances, due to that stronger constraints havebeen imposed on the states of the hidden Markov chains. Interestingly, we findthat the variability in model performance across households is significant,underscoring the importance of using larger scale data in the disaggregationproblem.

Interleaved Factorial Non-Homogeneous Hidden Markov Models for Energy Disaggregation