## 5 Perspectives and Conclusions  
Our methodology brings a fast system that can scale to any number of GPU-enabled machines. Computational run time results show increased performance compared to both conventional CPU usage (including cloud computing). Using masks in order to target the analysis on specific surfaces like building roofs significantly reduces calculation time. The application of the tool, as illustrated in the Greater Geneva area, enables estimating the solar energy potential of an arbitrary zone on the surface of the earth, at a high resolution, around the order of magnitude of the size of a regular photovoltaic panel. The proposed approach can be easily replicated everywhere in the world, provided that reliable input data, in particular DUSM, DTM, and building or roof spatial layers (for calculating energy indicators) are available. Weather data can be based on local meteorological stations or on statistical databases like Meteorom(r) that are available for areas around the world. Some limitations of our approach include the use of a raster model; by representing the environment by an elevation map, we cannot take into account overhanging structures, such as bridges, balconies, and arches. Such structures would be important to take into account if we were to adapt our model for determining the solar potential of vertical walls (facades). A vector model would be a lot more \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tile Name** & **Coverage Ratio** & **Run Time:** & **Seconds** & **Hours** \\ \hline Urban 1 & 25\% & With mask & 1961 & 0.5 \\  & & Without mask & 3546 & 1.0 \\ Urban 2 & 13\% & With mask & 1464 & 0.4 \\  & & Without mask & 3828 & 1.1 \\ Rural 1 & 84\% & With mask & 4233 & 1.2 \\  & & Without mask & 5004 & 1.4 \\ Rural 2 & 96\% & With mask & 4547 & 1.3 \\  & & Without mask & 4763 & 1.3 \\ Mountainous 1 & 50\% & With mask & 3356 & 0.9 \\  & & Without mask & 6241 & 1.7 \\  & & With mask & 4956 & 1.4 \\  & & Without mask & 5653 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measured run times of representative tiles, with and without the usage of a mask. appropriate for this purpose but will require more computationally intensive resources. A vector model would also allow for modeling sunlight bounces, for a more accurate determination of reflected solar energy, which, while negligible for rooftops, is a very important source of irradiation for facades. On the theoretical side, future perspectives can include the validation of our model with respect to ground truth (measured) data, by replacing our meteorological predictions by the actual a posteriori data corresponding to the weather that occurred. Performing benchmarks of the GPU-based systems referenced in Section 1 would be useful for the purpose of comparing shadow casting, irradiation and time calculation results, identify the most performant approach, and consider the effect of some simplifications. Another important analysis to be done is a sensitivity analysis, to determine to what extent increasing or decreasing the spatial or temporal resolution would have an effect on the final results. On the computational side and from an engineering perspective, further improvements to our method include taking into account the internal details of how the threading model of the GPU works, to make sure no processing potential is wasted. Such optimizations may be well indicated if we were to work on an area that is one or more orders of magnitude larger than what we are now processing. Another improvement would be to compute multiple direct illumination passes (for each hour of a given day for instance) in parallel on a GPU, saving the results in individual bits of a 32-bit integer, which will allow to save even more time, not only in terms of the actual computation, but also in terms of input/output speed.