# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 6 Conclusions  
In this paper, we presented a high-resolution rooftop photovoltaic potential estimation procedure based on GIS and satellite irradiance data, highly optimized to allow for calculation over large areas.  
We applied the procedure to the case of the Sardinia region in Italy. This gave us an estimate of the geographic, technical, and economic potential for the entire region with a spatial resolution of 1 m.  
The computation provided an estimate of photovoltaic potential on building roofs as a function of the cost of electricity, with an upper limit value of 22 TWh per year. This potential would abundantly cover the energy needs of the region, in particular those related to household consumption and for service activities directly associated with the buildings on which the panels could be installed. Energy is not power, however, and the need to  
Figure 14: Portion of the irradiation map in which the perimeter of some buildings does not follow the actual perimeter of the buildings themselves, but also erroneously includes the inner courtyards, thus misleading the algorithm that considers vegetation, for example, as an irregular cover. Surfaces with a slope exceeding 45\({}^{\circ}\) and not belonging to contiguous areas of at least 30 m\({}^{2}\) are, in any case, not considered in the calculation of municipal and regional photovoltaic potential.  
instantly balance demand and generation of electric power by ensuring the balance of the distribution system is clearly left out of this analysis. This work focused on estimating geographic potential as a basis for further processing, and the technical and economic analysis is based on standard technologies. We, therefore, make our results available to the community in the hope that other researchers could further investigate aspects of economic potential in relation to the different PV technologies available, and in relation to a changing regulatory and economic framework. The procedure is replicable in other areas where the necessary data are available. Our research will continue with the development of artificial intelligence techniques for identifying suitable areas for panel installation from satellite images.  
Conceptualization, A.P. and L.M.; methodology, A.P. and L.M.; software, A.P.; validation, A.P. and L.M.; formal analysis, L.M.; investigation, A.P.; resources, A.P.; data curation, A.P.; writing--original draft preparation, A.P.; writing--review and editing, L.M.; visualization, A.P.; supervision, L.M.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tessuto Digitale Metropolitano (TDM) project with funds provided by the Autonomous Region of Sardinia through Action 1.2.2 of the POR FESR Sardinia 2014-2020.  
Not applicable.  
The results presented in this paper are available upon request and are shown at [[http://scrs4.it/JM](http://scrs4.it/JM)]([http://scrs4.it/JM](http://scrs4.it/JM)) (accessed on 13 November 2022).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AOI & Angle of incidence \\ API & Application programming interface \\ CAPEX & Capital expenditure \\ DBGT & Geotopographic database \\ DHI & Diffuse horizontal irradiance \\ DNI & Direct normal irradiance \\ DSM & Digital surface model \\ GHI & Global horizontal irradiance \\ GIS & Geographic information system \\ LCOE & Levelized cost of electricity \\ LiDAR & Light detection and ranging \\ OPEX & Operating expense \\ POA & Plane of the array \\ PV & Photovoltaic \\ STC & Standard test conditions \\ TD & Tax deductions \\ \end{tabular}