Model for: A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes

Published: 10 Nov 2019 | Version 1 | DOI: 10.17632/f43xks8vyg.1
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Description of this data

The model is capable of creating stochastic multi-day occupancy profiles for building stock of different sizes and characterised by different shares of households belonging to the different occupancy categories identified in the UK. The model uses the Monte Carlo Markov Chain technique.
The occupancy categories are developed by the application of a data-mining clustering technique on data available from the UK Time Use Survey 2015. These categories are characterised by the following occupancy profiles:

  1. Daily absence: unoccupied period from 09.00 to 04:00,
  2. Working hours absence: unoccupied period from 08:20 to 18:10,
  3. Lunchtime absence: unoccupied period from 11:10 to 16:10,
  4. Constant presence 1,
  5. Constant presence 2.
    These occupancy categories are described in details in the associated paper and in a previous publication (https://doi.org/10.1016/j.enbuild.2019.05.056.).
    In the associated paper the stochastic occupancy profiles are used as inputs in energy models of residential buildings, but the source code may be readily adapted for specific applications, with due acknowledgement to the authors.

Experiment data files

This data is associated with the following publication:

A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes

Published in: Energy & Buildings

Latest version

  • Version 1

    2019-11-10

    Published: 2019-11-10

    DOI: 10.17632/f43xks8vyg.1

    Cite this dataset

    Buttitta, Giuseppina; Finn, Donal (2019), “Model for: A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes ”, Mendeley Data, v1 http://dx.doi.org/10.17632/f43xks8vyg.1

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Categories

Energy Use Behaviour, Modelling, Energy Modelling

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