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Conference Papers Year : 2021

Predicting the Window Opening State in an Office to Improve Indoor Air Quality

Abstract

Window operation is among one of the most influential factors on indoor air quality (IAQ). In this paper, we focus on the modeling of the windows’ opening state in a real open-plan office with five windows. The IAQ of this open-plan office was monitored over a whole year along with the opening state of the windows. A k-Nearest Neighbor (k-NN) classification model was implemented, based on a long time series of both indoor and outdoor monitored environmental factors such as temperature and relative humidity, and CO2 indoor concentration. In addition, the month, the day of the week and the time of the day were included. The obtained model for the window state prediction performs well with an accuracy of 92% for the training set and 86% for the testing set.
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Dates and versions

hal-04299096 , version 1 (04-12-2023)

Identifiers

  • HAL Id : hal-04299096 , version 1

Cite

Thi Hao Nguyen, Anda Ionescu, Olivier Ramalho, Evelyne Gehin. Predicting the Window Opening State in an Office to Improve Indoor Air Quality. International conference on Time Series and Forecasting ITISE 2021, Jul 2021, Gran Canaria, Spain. pp.24. ⟨hal-04299096⟩

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