An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

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22 Citations (Scopus)
40 Downloads (Pure)

Abstract

Household energy demand is closely correlated with occupant and household types and their associated occupancy patterns. Existing occupancy model performance has been limited by a lack of occupant differentiation, poor occupancy duration estimation, and ignoring typical occupancy interactions between related individuals. A Markov-Chain based method for generating realistic occupancy profiles has been developed that aims to improve accuracy in each of these areas to provide a foundation for future energy demand modelling and to allow the occupancy-driven impact to be determined. Transition probability data has been compiled for multiple occupant, household, and day types from UK Time-Use Survey data to account for typical behavioural differences. A higher-order method incorporating ranges of occupancy state durations has been used to improve duration prediction. Typical occupant interactions have been captured by combining couples and parents as single entities and linking parent and child occupancy directly. Significant improvement in occupancy prediction is shown for the differentiated occupant and occupant interaction methods. The higher-order Markov method is shown to perform better than an equivalent higher-order ’event’-based approach. The benefit of the higher-order method compared to a first-order Markov model is less significant and would benefit from more comprehensive occupancy data for an objective comparison.
Original languageEnglish
Pages (from-to)219-230
Number of pages12
JournalEnergy and Buildings
Volume125
Early online date9 May 2016
DOIs
Publication statusPublished - 1 Aug 2016

Keywords

  • markov chain
  • higher-order
  • distributed generation
  • microgeneration
  • energy demand
  • modelling
  • domestic
  • occupancy

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    • 22 Citations
    • 1 Article
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  • 14 Citations (Scopus)
    40 Downloads (Pure)

    Impacts

    Building performance simulation with computational modelling software enables practitioners to realise a low carbon built environment.

    Joseph Andrew Clarke (Participant), Nicolas Kelly (Participant) & Paul Strachan (Participant)

    Impact: Impact - for External PortalEnvironment and sustainability - natural world and built environment, Professional practice, training and standards

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