A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand

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Abstract

An integrated domestic occupancy and demand model with a 1-min resolution has been developed which better captures the influence of different occupant behaviours than previous models. The occupancy model includes the fundamental link between occupancy and demand, and differentiates between different types and sizes of households. In particular, the likelihood of daytime occupancy is captured by age and employment differentiators. A novel method for identifying appliance use events and linking use to an occupancy profile has been developed that accounts for household specific appliance usage using an event-based approach calibrated directly from measured data. The method has been shown to perform better than both per-timestep probability models and models calibrated from time-use survey activity diaries. To further capture individual household behaviours, the demand model incorporates additional factoring to account for income and random behavioural influences. Whilst improving differentiation of individual household energy usage, due to limitations in the available data, the model incorporates some occupancy and use behaviour factors that are a composite of multiple households, leading to some behaviour averaging in the model output; consequently the model is best employed for energy demand assessment of multiple households.

Original languageEnglish
Pages (from-to)171-187
Number of pages17
JournalEnergy and Buildings
Volume140
Early online date31 Jan 2017
DOIs
Publication statusPublished - 1 Apr 2017

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Keywords

  • demand
  • occupancy modelling
  • disaggregated
  • building simulation
  • probabilistic model

Cite this

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title = "A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand",
abstract = "An integrated domestic occupancy and demand model with a 1-min resolution has been developed which better captures the influence of different occupant behaviours than previous models. The occupancy model includes the fundamental link between occupancy and demand, and differentiates between different types and sizes of households. In particular, the likelihood of daytime occupancy is captured by age and employment differentiators. A novel method for identifying appliance use events and linking use to an occupancy profile has been developed that accounts for household specific appliance usage using an event-based approach calibrated directly from measured data. The method has been shown to perform better than both per-timestep probability models and models calibrated from time-use survey activity diaries. To further capture individual household behaviours, the demand model incorporates additional factoring to account for income and random behavioural influences. Whilst improving differentiation of individual household energy usage, due to limitations in the available data, the model incorporates some occupancy and use behaviour factors that are a composite of multiple households, leading to some behaviour averaging in the model output; consequently the model is best employed for energy demand assessment of multiple households.",
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author = "Graeme Flett and Nick Kelly",
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AB - An integrated domestic occupancy and demand model with a 1-min resolution has been developed which better captures the influence of different occupant behaviours than previous models. The occupancy model includes the fundamental link between occupancy and demand, and differentiates between different types and sizes of households. In particular, the likelihood of daytime occupancy is captured by age and employment differentiators. A novel method for identifying appliance use events and linking use to an occupancy profile has been developed that accounts for household specific appliance usage using an event-based approach calibrated directly from measured data. The method has been shown to perform better than both per-timestep probability models and models calibrated from time-use survey activity diaries. To further capture individual household behaviours, the demand model incorporates additional factoring to account for income and random behavioural influences. Whilst improving differentiation of individual household energy usage, due to limitations in the available data, the model incorporates some occupancy and use behaviour factors that are a composite of multiple households, leading to some behaviour averaging in the model output; consequently the model is best employed for energy demand assessment of multiple households.

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KW - disaggregated

KW - building simulation

KW - probabilistic model

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