A Markov-Switching model for building occupant activity estimation

Sebastian Wolf*, Jan Kloppenborg Møller, Magnus Alexander Bitsch, John Krogstie, Henrik Madsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Heating and ventilation strategies in buildings can be improved significantly if information about the current presence and activity level of the occupants is taken into account. Therefore, there is a high demand for inexpensive sensor-based methods to detect the occupancy or occupant activity level. It is shown that the carbon dioxide (CO2) level in a room is dependent on the activity level rather than only on just the number of people. Therefore, this study suggests a new model based on the use of CO2 trajectories to estimate the occupant activity level, trained on measurements both from a school classroom and from a Danish summerhouse. A hidden Markov-switching model was employed to identify the activity level. This modelling approach is a generalization of hidden Markov models, taking autocorrelation in the observations into account. This is done by an additional autoregressive part which models the persistence of the CO2 concentration by relating the current value to past lags. The analysis of one-step prediction residuals shows that this method inherits the dynamics of the CO2 curves much better than an ordinary hidden Markov model, and can therefore be considered a promising candidate for occupant activity estimation. Furthermore, it is shown that the presented model can be used for simulations of activity level and of the accompanying CO2 levels.

Original languageEnglish
JournalEnergy and Buildings
Volume183
Pages (from-to)672-683
Number of pages12
ISSN0378-7788
DOIs
Publication statusPublished - 2019

Keywords

  • Hidden Markov models
  • Occupancy detection
  • Occupant activity modelling
  • Occupant behaviour

Cite this

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title = "A Markov-Switching model for building occupant activity estimation",
abstract = "Heating and ventilation strategies in buildings can be improved significantly if information about the current presence and activity level of the occupants is taken into account. Therefore, there is a high demand for inexpensive sensor-based methods to detect the occupancy or occupant activity level. It is shown that the carbon dioxide (CO2) level in a room is dependent on the activity level rather than only on just the number of people. Therefore, this study suggests a new model based on the use of CO2 trajectories to estimate the occupant activity level, trained on measurements both from a school classroom and from a Danish summerhouse. A hidden Markov-switching model was employed to identify the activity level. This modelling approach is a generalization of hidden Markov models, taking autocorrelation in the observations into account. This is done by an additional autoregressive part which models the persistence of the CO2 concentration by relating the current value to past lags. The analysis of one-step prediction residuals shows that this method inherits the dynamics of the CO2 curves much better than an ordinary hidden Markov model, and can therefore be considered a promising candidate for occupant activity estimation. Furthermore, it is shown that the presented model can be used for simulations of activity level and of the accompanying CO2 levels.",
keywords = "Hidden Markov models, Occupancy detection, Occupant activity modelling, Occupant behaviour",
author = "Sebastian Wolf and M{\o}ller, {Jan Kloppenborg} and Bitsch, {Magnus Alexander} and John Krogstie and Henrik Madsen",
year = "2019",
doi = "10.1016/j.enbuild.2018.11.041",
language = "English",
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journal = "Energy and Buildings",
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A Markov-Switching model for building occupant activity estimation. / Wolf, Sebastian; Møller, Jan Kloppenborg; Bitsch, Magnus Alexander; Krogstie, John; Madsen, Henrik.

In: Energy and Buildings, Vol. 183, 2019, p. 672-683.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A Markov-Switching model for building occupant activity estimation

AU - Wolf, Sebastian

AU - Møller, Jan Kloppenborg

AU - Bitsch, Magnus Alexander

AU - Krogstie, John

AU - Madsen, Henrik

PY - 2019

Y1 - 2019

N2 - Heating and ventilation strategies in buildings can be improved significantly if information about the current presence and activity level of the occupants is taken into account. Therefore, there is a high demand for inexpensive sensor-based methods to detect the occupancy or occupant activity level. It is shown that the carbon dioxide (CO2) level in a room is dependent on the activity level rather than only on just the number of people. Therefore, this study suggests a new model based on the use of CO2 trajectories to estimate the occupant activity level, trained on measurements both from a school classroom and from a Danish summerhouse. A hidden Markov-switching model was employed to identify the activity level. This modelling approach is a generalization of hidden Markov models, taking autocorrelation in the observations into account. This is done by an additional autoregressive part which models the persistence of the CO2 concentration by relating the current value to past lags. The analysis of one-step prediction residuals shows that this method inherits the dynamics of the CO2 curves much better than an ordinary hidden Markov model, and can therefore be considered a promising candidate for occupant activity estimation. Furthermore, it is shown that the presented model can be used for simulations of activity level and of the accompanying CO2 levels.

AB - Heating and ventilation strategies in buildings can be improved significantly if information about the current presence and activity level of the occupants is taken into account. Therefore, there is a high demand for inexpensive sensor-based methods to detect the occupancy or occupant activity level. It is shown that the carbon dioxide (CO2) level in a room is dependent on the activity level rather than only on just the number of people. Therefore, this study suggests a new model based on the use of CO2 trajectories to estimate the occupant activity level, trained on measurements both from a school classroom and from a Danish summerhouse. A hidden Markov-switching model was employed to identify the activity level. This modelling approach is a generalization of hidden Markov models, taking autocorrelation in the observations into account. This is done by an additional autoregressive part which models the persistence of the CO2 concentration by relating the current value to past lags. The analysis of one-step prediction residuals shows that this method inherits the dynamics of the CO2 curves much better than an ordinary hidden Markov model, and can therefore be considered a promising candidate for occupant activity estimation. Furthermore, it is shown that the presented model can be used for simulations of activity level and of the accompanying CO2 levels.

KW - Hidden Markov models

KW - Occupancy detection

KW - Occupant activity modelling

KW - Occupant behaviour

U2 - 10.1016/j.enbuild.2018.11.041

DO - 10.1016/j.enbuild.2018.11.041

M3 - Journal article

VL - 183

SP - 672

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JO - Energy and Buildings

JF - Energy and Buildings

SN - 0378-7788

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