# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 2 Experimental Design
### Apartments  
Five apartments were included in this study. They were located in Wroclaw (Poland). To obtain a diverse sample, we chose apartments that had different surroundings and standards and were used differently. The flats were distributed over various residential areas and located in buildings of different types and ages. The apartments' size (from spacious apartments to studios), space arrangement (open kitchen and closed kitchen), as well as furnishing (from new and very modern to old and used) and appliances (type of ventilation, cooker, etc. ), were different. Each apartment was occupied by one family, i.e., a couple or a couple with one child, which is typical in the city. However, we chose families with different cultural backgrounds, which implied specific lifestyles as well as individual dietary and cooking habits. More details regarding the characteristics of the individual apartments are presented in Table 2. The inhabitants were asked to live their lives as usual to obtain representative results from the IAQ monitoring study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensor** & **Measured** & **Detection** & **Measurement** & **Accuracy** & **Resolution** & **Repeatability** & **Long-Term Drift** \\ \hline \multirow{4}{*}{SHT 25} & \multirow{2}{*}{T} & Bandgap & \(-40\) to 125 & \multirow{2}{*}{Typ. \(\pm 0.2\)\({}^{\circ}\)C} & \multirow{2}{*}{0.04 \({}^{\circ}\)C} & \multirow{2}{*}{\(\pm 0.1\)\({}^{\circ}\)C} & \multirow{2}{*}{\(<\)0.02 \({}^{\circ}\)C/yr} \\  & & temperature & \({}^{\circ}\)C & & & & \\ \cline{2-7}  & & sensor & & & & & \\ \cline{2-7}  & \multirow{2}{*}{RH} & Capacity-type & \multirow{2}{*}{0 to 95\% RH} & \(\pm 1.8\)\%RH & \multirow{2}{*}{0.04\%RH} & \multirow{2}{*}{\(\pm 0.1\)\%RH} & \multirow{2}{*}{\(<\)0.25\%RH/yr} \\  & & humidity sensor & & & & & \\ \hline \multirow{2}{*}{SCD30} & \multirow{2}{*}{CO\({}_{2}\)} & Non-dispersive & \multirow{2}{*}{0-500 ppm} & \(\pm\)(30 ppm + & \multirow{2}{*}{-} & \multirow{2}{*}{\(\pm 10\) ppm} & \multirow{2}{*}{\(\pm 50\) ppm} \\  & & infrared (NDIR) & & & & & \\ \cline{2-7}  & & & & & & & \\ \hline \multirow{2}{*}{SGP30} & \multirow{2}{*}{TVOCs} & Metal oxide gas & 0.3-30 ppm & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Typ. 0.2\% of} & \multirow{2}{*}{-} & \multirow{2}{*}{Typ. 1.3\% of} \\  & and CO\({}_{2}\)eq & & & & & & \\ \cline{2-7}  & & & & & & & \\ \hline \multirow{2}{*}{SGPC3} & \multirow{2}{*}{TVOCs} & Metal oxide gas & 0.3-30 ppm & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Typ. 15\% of} & \multirow{2}{*}{Typ. 0.2\% of} & \multirow{2}{*}{-} & \multirow{2}{*}{Typ. 1.3\% of} \\  & and CO\({}_{2}\)eq & & & & & & \\ \cline{2-7}  & & & & & & & \\ \hline \multirow{2}{*}{SGPC3} & \multirow{2}{*}{TVOCs} & Metal oxide gas & 0.3-30 ppm & \multirow{2}{*}{Typ. 15\% of} & \multirow{2}{*}{Typ. 0.2\% of} & \multirow{2}{*}{-} & \multirow{2}{*}{Typ. 1.3\% of} \\  & sensor (chemical & 0-1000 ppm & & & & & \\ \cline{2-7}  & & ethanol & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measurement characteristics of sensors applied in the multi-sensor device [34].