## 5 Results and Discussions  
With the proposed architecture, we observed that, from the implemented components, we could obtain a robust application fed by various services that allow users to have transparency in their energy consumption information.  
For the AI component, we measured the accuracy, as shown in Table 5. We consider evaluating other types of models to give even greater robustness to the AI component, and which will be studied in future research.  
The information was handled quickly and versatilely regarding the means of communication to send the data with the developed sensor.  
On the other hand, prototype of a mobile application meets the functional requirements previously stated in Table 6 for managing energy consumption and demand data through a distributed process and serving as a prelude to structuring a much more robust mobile architecture in the framework of the SGs. Figure 11 shows the interface of the proposed mobile application within the architecture.  
The application consists of four sections, which are:  
* Dashboard: In this panel, the user can see the location of his home, the total energy consumption in watts consumed, appliance consumption, real-time consumption, and billing.
* Functions: Here, the user gets to see a menu that allows him/her to use the map functionalities to see his/her location, electric vehicle charging stations, and maps of electricity generation, transmission, and distribution centers in the province Panama.
* Analytics: In this panel, the user can see predictions about energy consumption patterns, and the system offers a series of recommendations based on previous data. In addition, the user can consult accurate data from Panama regarding electricity generation.
* Profile: In this section, users can view their registration data, passwords, and security issues.  
This study uses SUS adapted with adjective rating scale to measure the usability of mobile applications. To obtain the results of the usability test of the application, the average results obtained from the user questionnaire are added up, considering the following: the  
Figure 11: Mobile interface of the prototype application.  
odd questions (1, 3, 5, 7, and 9) will take the value assigned by the user, and 1 will be subtracted from it.  
For the even questions (2, 4, 6, 8, 10), it will be 5 minus the assigned value. Once the final number is obtained, it is multiplied by 2.5. Table 9 shows the results obtained from the application of the test to the users.  
According to what was described above in the use of the SUS scale, we have that:  
According to the data in the graphs found in the table, the results obtained in the questions, in order, were: 5,1,5,2,5,2,2,4,1,4,1.  
We assign the new values according to the SUS algorithm, and our new values will be:  
\[((5-1)+(5-1)+(5-1)+(5-2)+(5-1)+(5-2)+(4-1)+(5-1)+(4-1)+(5-1))\ {}^{\star}2.5\]  
Then, the SUS score is 90. Given that the theoretical maximum is 100 points and looking at the Figure 10 in the range of adjectives, this result indicates that the level of user satisfaction in using the application is excellent.  
The proposed architecture model shows that investment in energy transformation can significantly influence future socioeconomic development and position Panama as a clean technology energy hub at the forefront of the Latin American and Caribbean region.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results for the user satisfaction validation technique (SUS Scale).  
For the projections to be fully achieved, it is necessary to modernize and innovate by aligning the governance of energy policy and international cooperation, expanding the supply of innovation aimed at the energy sector, addressing critical gaps, increasing the demand for clean and sustainable energy technologies and innovation, taking advantage of digitalization while closing the digital divide, improving data collection, management and application, and data systems.