## 6 Results  
To analyze the results of the usability and user experience tests, we first organized the SUS scores into a percentile chart. We summed the mean scores from the user questionnaire, and with a mean score of 68, we reached a total score of 80 points, as shown in Figure 12. This result reflects a very satisfactory user experience with the interface, indicating an acceptable and promising margin. On the other hand, the UEQ-S results reveal a generally positive experience with the evaluated energetic mobile interface, exceeding the average. The scores indicate a high degree of satisfaction with both the usability (pragmatic quality) and the emotional and aesthetic aspects (hedonic quality) of the user experience, as seen in Figure 13.  
Figure 11: Defuzzification by the centroid method. Figure 12: Percentage representation of SUS results. Finally, in the fuzzy logic system, we have the input values for the indicators of ease of use, satisfaction, and efficiency, respectively. In the defuzzification process, the degrees of relevance of each of the input variables are calculated for each of their labels. When defuzzification is applied to the input variables, each with numerical values associated with each one of them, the results of Table 12 are obtained. The calculation of the degrees of membership is performed according to the typical membership functions. The fuzzy inference process is performed through the defined rules, and once this process has been carried out, the values shown in Table 13 are obtained for the output variables. Figure 14 shows the graphs with the satisfaction results, in which the model indicates that the level of satisfaction with the interface and the ease of use are relatively high. The efficiency is shown as medium and high, which are fundamental since they define how the quantitative values are converted into fuzzy qualitative ratings. The inference rules then use these ratings to determine the system's outputs based on the given inputs. They are the visual representation of how the input maps to a degree of membership in each of the fuzzy sets defined for each variable. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Fuzzy Set**} & \multicolumn{2}{c}{**Degree of Membership**} & \multicolumn{1}{c}{**Degree of Membership**} \\  & \multicolumn{1}{c}{**(Probability of Recommendation)**} & \multicolumn{1}{c}{**(Quality of UX)**} \\ \hline Low & 0.2 & 0.6 \\ \hline Medium & 0.8 & 0.3 \\ \hline High & 0.5 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Degrees of membership of the output variable to the fuzzy sets. Figure 14: Results for input variables. Figure 13: User experience quality evaluation: UEQ-S benchmarking. This model allows a deeper understanding of the evaluations made through the User Experience Questionnaire Short (UEQ-S) and the System Usability Scale (SUS). These numerical values are the result of the defuzzification of the fuzzy logic model and represent the composite evaluation of the system we are analyzing and how results are obtained. * Figure 15 shows the probability of recommendation (83.33): This value suggests that there is a high probability that users will recommend the mobile interface system for residential energy management. In fuzzy logic, this is deduced from rules that associate inputs of high ease of use and high satisfaction with a high probability of recommendation. Since both ease of use and satisfaction were rated high, the fuzzy inference results in a positive rating. It could be interpreted as an indicator that users are satisfied with the system and willing to share their positive experiences with others. * Figure 16 shows the overall quality of user experience (77.98): This value indicates that the overall quality of user experience with the system is also high. It has been calculated considering ease of use satisfaction and the efficiency of user tasks. It suggests that users find the system easy to use and satisfying and that it allows for efficient interaction.