This dataset contains municipal solar resource data, generated using the toscana tool.

Dataset description Each file corresponds to a specific village. Two types of files are available in this dataset : a solar cadastre in raster format, representing the annual solar irradiation (in kWh/m²) received by the surfaces, and a file containing the average annual solar irradiation per building (in kWh/m²) in shapefile format.

Study context This dataset was created within the frame of a study analyzing the impact of topography on rooftop solar potential. In this study, 92 French villages were simulated, that are located in the Alps and in the Rhône Valley. This study has made the object of a publication in Solar Energy, 2024, doi.org/10.1016/j.solener.2024.112632

To extend the research, the dataset was supplemented with 100 additional villages to investigate the combined effect of topography and local parameters, such as local climate, urban planning, dataset, and variations in topographic features. These other villages are located in the French Pyrenees, in mountainous regions of foreign countries (Spain, Italy, Switzerland, Germany) and in French lowland areas other than the Rhône Valley.

The data are categorized by village, and villages are further classified based on their geographical location. French mountain villages are stored in the "Alps" or "Pyrenees" folders, while lowland villages are found in "Rhône_Valley" and "Other_plain_villages" folders. Foreign mountain villages are in the "Foreign_mountain_villages" folder and further subdivided by country.

Methodology The metholodology used to obtain municipal solar resource data is described in detail in a publication ( https://doi.org/10.1016/j.solener.2024.112632) and in the documentation of the toscana package, available on GitHub at the following address : https://github.com/locie/toscana/tree/main/doc/_build.
The BDTOPO database is used to retrieve the municipal boundaries and building footprints, while the EU-DEM database is used to provide the DEM. QGIS functions are used to process geographical data, create a DSM and divide the territory into tiles to reduce the computational time. Meteorological data (Typical Meteorological Year) are retrieved from PVGIS for each tile and averaged weather files are used in the simulations to account for the difference in spatial resolution between the tiles and the meteorological database. Solar energy simulations are conducted using the SEBE algorithm for each tile. Finally, QGIS functions are applied to process and generate the final output files.

QGIS, 3.32

python, 3.9.16

UMEP, 4.04