Machine Learning-Based Indoor Relative Humidity and CO₂ Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Authors

Yassine El Mendili, Normandie Université
Mohammed Hichem Benzaama, Normandie Université
Karim Touati, Normandie Université
Malo Le Guern, Normandie Université
François Streiff, Parc Naturel Régional des Marais du Cotentin et du Bessin
Steve Goodhew, School of Art, Design and Architecture

ORCID

• Steve Goodhew: 0000-0003-1227-217X

Abstract

The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO₂, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO₂ concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO₂ in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO₂ content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO₂ levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO₂ levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO₂ levels in indoor environments.

DOI

10.3390/en17010243

Publication Date

2024-01-03

Publication Title

Energies

Volume

17

Issue

1

ISSN

1996-1073

Keywords

artificial neural network, cob, indoor air quality, indoor relative humidity, prediction, PWARX model

Recommended Citation

El Mendili, Y., Benzaama, M., Touati, K., Le Guern, M., Streiff, F., & Goodhew, S. (2024) 'Machine Learning-Based Indoor Relative Humidity and CO₂ Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study', Energies, 17(1). Available at: 10.3390/en17010243" >https://doi.org/10.3390/en17010243