Machine Learning-Based Indoor Relative Humidity and CO[sub.2] Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

• Published in: Energies (Basel) Vol. 17; no. 1
• Main Authors: Benzaama, Mohammed-Hichem, Touati, Karim, El Mendili, Yassine, Le Guern, Malo, Streiff, François, Goodhew, Steve
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.01.2024
• Subjects: Indoor air quality; Machine learning; Sensors; France
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17010243

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. CO[sub.2], one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO[sub.2] 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 CO[sub.2] 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 CO[sub.2] 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 CO[sub.2] 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 CO[sub.2] levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO[sub.2] levels in indoor environments.