# A Methodology to Improve Energy Efficiency and Sustainability in Urban Environments
## 4 Solar Potential Study
### Simplified Solar Potential Value Moderation  
The first procedure developed focuses on obtaining reliable data compared to the real situation, calculating the solar potential using a simplified model. In this regard, a methodology was developed that can take into account the data obtained through simulations, supplementing the results with some corrective coefficients in order to make the data as close as possible to reality. This is necessary because each building has particular characteristics due to the presence of windows, balconies, shading, particular exterior overhangs, and canopies. These factors affect both the area available for the installation of new photovoltaic systems and their efficiency [42]. These specific conditions are not considered at all by analyzing the solar potential using a simplified model. This could be obtained from a spatial database and generated with scripts using, for example, the Urbano  
Figure 9: Absolute difference of the potential air temperature between AS and CPS. tool, starting from an.osm file (Figure 10). The comparison between the simplified and actual situation highlighted a substantial difference. To avoid the problem related to the extreme simplification of the data, the proposed strategy was to find some correction coefficients that, when multiplied with the simplified input, can return a result very close to the solar potential calculated in a detailed model [43]. The formulated equation establishes a correlation between the real solar potential (\(SP_{s}\)) and the simplified solar potential (\(SP_{r}\)). It takes into account the percentage of opaque surface area (\(\%op_{area}\)) as well as a previously calculated transformation coefficient (\(TC_{\text{\it{off}}}\)), as described in a paper co-published by the research group of the ICEA department of the University of Padua, coordinated by Professor [PERSON] [44]:  
\[SP_{r}=SP_{s}\cdot\%op_{area}\cdot TC_{\text{\it{off}}}, \tag{1}\]  
The process of calculation of these indices involved visual and geometric surveys to evaluate the properties of the various surfaces and areas available [45]. This process had certain limitations, as it was neither immediate nor rapid. However, advancements in technology such as the use of drones and photogrammetry could potentially streamline and expedite the process. In reference to the mentioned case study, two particular buildings were chosen to test this methodology. They comprise an office building and another one with laboratories, situated adjacent to the first one on the east side of the intervention area. For each building, the necessary values for calculating and defining the transformation coefficient were determined for each facade. These values included the total surface area, the area occupied by windows (glazed surface), and the area occupied by solid walls (opaque surface). By considering these parameters, the percentages of glazed and opaque areas for each facade were determined. Additionally, the solar potential was calculated for both the application of a simplified model and a more detailed model, allowing for a comparison of results (Figure 11). To ensure consistency in the data, an arithmetic average was calculated based on the geometric values obtained. This averaging process aimed to harmonize the different conformational characteristics of the surfaces, taking into account their varying degrees of restrictiveness. Once the transformation coefficient was determined, it was incorporated into the calculation of the simplified solar potential, within the Grasshopper script. This adjustment enabled the generation of more accurate data that reflected the actual characteristics of the buildings. By comparing these adjusted results with the detailed solar potential data obtained for the two selected buildings, the accuracy and reliability of the output could be evaluated.