## 7 Discussion  
The thermal comfort results from the inference networks were compared with the work environments selected for data mining.  
Furthermore, new values obtained from the surveys in different industries of the northern area were tested, thus creating new evidence cases whose results are comparable with those provided by the staff in the working environment, so no correction to the network probabilities was necessary.  
It can be noted that, apart from the possible building design optimization, thermal comfort depends on temperature, work type, time of day, season, and relative humidity percentage.  
In the northern area of Argentina, due to its warm subtropical climate, staff can endure maximum temperatures of up to 30 \({}^{\circ}\)C within humidity limits without their optimal comfort level being affected. It can be concluded that human beings have a certain climate adaptability.  
It is also worth noting that comfort parameters are similar for both summer and winter as for the different activities in the northern area. It can be concluded that this characteristic is due to such adaptability.  
The J48 WEKA classifier obtained results corresponding to those informed in the study areas and was the basis for the network formulation with ELVIRA software and the result verification.  
The use of Bayesian inference techniques reduces error probabilities for the results obtained with statistical analysis and offers a greater number of temperature-humidity relationships based on the variables (activity, time of day, season) to reach a specific comfort level.  
To verify what has been previously mentioned, a comparative study between different adaptive models ([PERSON], [PERSON] et al. [14]) and the values obtained from the data mined in the study area by using linear regression techniques was performed. In the case of adaptive models, comfort temperature (tn) was calculated based on outdoor mean temperature (tme) [44]. For linear regression technique calculations, a partial data set [45] was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **Time** & **Temperature** & **R.H.\%** & **Activity** & **Comfort** \\ \hline \multirow{2}{*}{Winter} & \multirow{2}{*}{10.00 h} & \textgreater{}19 \({}^{\circ}\)C and & \multirow{2}{*}{\(\leq\)54\%} & \multirow{2}{*}{Light} & \multirow{2}{*}{100\% High} \\  & & \(\leq\)25 \({}^{\circ}\)C & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results from the first evidence case—Winter.  
Figure 7: ELVIRA—Inference Network—First evidence case—Northern area, Winter.  
used without distinguishing activity type, time schedule, season, or humidity percentage. Tables 6 and 7 respectively show results for summer and winter.  
When analyzing Tables 6 and 7, it can be noted that the differences between the obtained results are not significantly different between the adaptive models and those obtained from linear regression in our case study [45]. However, it should be considered that results calculated with linear regression were obtained from a partial data set since adaptive models only provide one comfort temperature without considering variables such as time of year, time schedule, or activity in the conditioned industry premises.  
If these results are compared with those obtained--for example, with the first evidence case from Bayesian networks (Tables 4 and 5)--it can be concluded that they are within the same range, yet the network provides more information such as temperature, relative humidity percentage, activity type, time schedule, and time of year for a specific comfort probability.  
On the other hand, the Fanger or quantitative method does not consider the actual working conditions since it is based on a static model of heat exchange, nor does it consider human environment adaptability.  
The Bayesian inference model's contribution is offering a variety of design parameter alternatives (indoor temperature/relative humidity percentage relationships) for different working conditions in industries while considering time and season.