## 4 Results
### Data Comparison  
The first comparison was between the designed weather station and the Schneider Electric thermostat. As stated, the two devices were placed together, as shown in Figure 7. The data from the reference instrument were provided directly by Schneider's personnel. Figure 18 shows the two signals; the blue line represents the measurements performed by the current proposal, and the orange one is the reference value. At first glance, the two signals seem to have the same direction, and a monotonic relationship can be inferred. Statistical parameters were obtained to compare the two signals and determine their relationship. The first non-parametric value is the Spearman correlation, which indicates the strength of the association, a number between \(-1\) and \(1\), and the direction of the association, which can be positive or negative [40]. Additionally, the root mean square error (RMSE) and mean absolute percentage error MAPE were calculated to determine the performance of the measurement process; these parameters are mainly focused on rating a forecast model's accuracy, but they can be applied to quantify the total error of two signals. Moreover, a Bland-Altman plot was generated to assess the agreement between these two signals. On the \(y\)-axis, the differences between the measurements are displayed; on the \(x\)-axis, the averages of the measurements are plotted. Each plot on this graph is a pair of measurements taken by the two instruments. Finally, this Bland-Altman method has two limits of agreement at \(\pm 1.96\) times the standard deviation. Ideally, all the points should lie between these two limits, representing an agreement between them [41]. Figure 19 shows the Bland-Altman plot generated by comparing the time series. This figure indicates a good agreement between these signals since most points are between the interval limits [41]. The differences between the paired values increase when the temperature is between 23.6 \({}^{\circ}\)C and 23.8 \({}^{\circ}\)C. Table 6 shows the values obtained for the other statistical parameters. The correlation parameter is a high value indicating a strong relationship between the two measurements [42]. In addition, RMSE and MAPE are low values, demonstrating that the OSWS has similar measurement values to [PERSON]'s thermostat. Figure 18: OSWS and reference device plot in an indoor scenario for temperature. In assessing humidity measurements, Figure 20 illustrates a comparative analysis between the proposed OSWS and the [PERSON]'s thermostat over a specified time frame. Concurrently, Figure 21 presents the Bland-Altman plot, highlighting that most values reside within the stipulated boundaries. This pattern suggests a favorable level of agreement between the two measurements (air temperature and relative humidity) since most points are between the two red lines representing the limits of agreement in which 95% of the differences between the two measurements are expected to fall. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Spearman Correlation** & **RMSE** & **MAPE** \\ \hline
0.96 & 0.19 °C & 0.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical parameters for temperature measurements. Figure 20: OSWS and reference device in an indoor scenario for relative humidity. The OSWS was juxtaposed with a Purple Air sensor located approximately 2.118 km away in the outdoor environment. The methodologies employed were consistent with those used in the indoor experiments. Comprehensive quantitative findings are enumerated in Table 7. Moreover, Figure 22 depicts the atmospheric pressure graphs where the upper blue line represents the signal from the proposed station. In contrast, the red line at the bottom corresponds to the reference sensor. Notably, the difference in readings between the two signals in Figure 22 could be attributed to the variance in altitude where each sensor was positioned. Figure 23 highlights the measurement discrepancy, revealing an average difference of 1.07 hPa.