## 5 Discussion  
The findings derived from the result analysis of the two use cases can be interpreted in the following manner.  
* The results of the numerical variable in the first use case are deemed acceptable. The predicted U value for roofs was 0.1. The thickness value for wall insulation was found to be 0.25. The predicted value for roof insulation was 0.3. Lastly, the U value for windows was measured to be 0.55. These values are within the acceptable ranges mentioned in Table 4. However, the results for the categorical variables are inferior, since the obtained accuracy values range between 50% and 60%. This can be attributed to the limited quantity of building data that were taken into account. To train the ML algorithms, data were taken from the Passive House database in addition to the IFC BIM file of CERTH's smart home. Since the data were collected manually from the database, data of only 30 buildings were considered in the database. It is also noted that when data were generated artificially, accuracy decreased even further. The utilization of larger datasets has the potential to result in increased accuracy when dealing with categorical variables.
* In addition to conducting a quantitative evaluation of the outcomes of the proposed RS, a qualitative evaluation was also implemented to assess the coherence and validity of the results. For that purpose, different sets of input variables were tested to observe the thickness of insulation and the corresponding U values of the wall and roof. It was observed that when there is an increase in the thickness of the wall and roof insulation, the U values tend to drop. This is in line with the concepts of thermal resistance. An increase in insulation thickness would increase thermal resistance and therefore decrease the U values because the two are inversely proportional. Thus, it was determined that the results obtained from the proposed RS make sense from a domain-knowledge point of view.
* Second use case
* The challenging part about developing an algorithm for an RS using SRI data was the difficulty in finding complete data. As mentioned in previous sections, the SRI data for training the ML algorithm were taken from the available published literature. However, this was only data for 11 buildings, and the results as such would not be very good for a wide range of buildings when using such a small dataset. As a result, out of every three input instances in the RS, two values were predicted correctly.
* From a qualitative perspective, there is a favorable relationship between the year of construction and the SRI score. This means that for a given smart-ready service, the functionality level should be higher if the construction year input by the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Actual Functionality Level** & **Predicted Functionality Level** \\ \hline
1 & 0 \\
0 & 1.5 \\
1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Decision tree algorithm performance.  
user is more recent. If the building is old, then it should be lower. This test was performed, and the RS produced the expected results, suggesting a higher functionality level for the same smart-ready service if the building is new; but if it is old, lower functionality levels are suggested.