MODERATE Solar Cadastre Dataset

This dataset contains building and rooftop characteristics for the city of Crevillent , Spain, together with estimated photovoltaic (PV) potential indicators, including suitable roof area, installable PV capacity, expected yearly electricity production, specific PV yield, and potential CO₂ emission savings. The PV potential assessment combines building footprints, annual irradiance data, terrain geometry, and shading analysis using a digital elevation model (DEM), while PV performance is derived from PVGIS and processed with PVLIB. Only technically and economically feasible rooftop areas are included. The dataset includes the following entries: Column Explanation ID Unique identifier of the building or roof section b_area Building or roof subsection area used for the calculation (in m²) p_area Total parcel or roof area associated with the building (m²) building_u Building use / building category year_cons Year of construction floors Number of floors of the building dwellings Number of residential units/apartments r_typology Residential typology thermal_ne Thermal need – Annual thermal energy demand of the building parcel, in MWh/year area_conv Roof area convenient/suitable for PV installation (m²) – this area is used for PV calculations PV_nominal Estimated installed PV peak power (kWp) PV_potential Estimated yearly PV electricity production (kWh/year) average_yi Specific PV yield (kWh/kWp/year) potentialC… Potential annual CO₂ emissions avoided (kg CO₂/year) centroid_l Latitude of building centroid centroid_1 Longitude of building centroid centroid_x X coordinate in projected coordinate system centroid_y Y coordinate in projected coordinate system Derivation of the dataset: The dataset combines building footprints with a spatial annual irradiance raster (e.g., a local solar‑cadastre / processed irradiance layer) and a digital elevation model (DEM) to capture site geometry and shading; hourly PV performance and solar geometry come from PVGIS [1] and are processed with PVLIB [2] for accurate sun positions; PVGIS hourly profiles are horizon‑corrected (using the DEM) and then scaled to match the raster’s annual totals. Finally, a set of internal technical and economic assumptions (module efficiency, performance ratio, self‑consumption/export tariffs, investment cost, discount rate, lifetime, etc.) converts energy into annual revenue and Net Present Value (NPV), and only pixels with positive NPV are considered feasible—producing per‑building feasible area, nominal capacity, annual generation (post‑shading, raster‑calibrated), specific yield, and estimated CO₂. [1] Photovoltaic Geographical Information System (PVGIS), European Commission Joint Research Centre. Available online: https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis_en [2] Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. “pvlib python: 2023 project update.” Journal of Open Source Software, 8(92), 5994, (2023). DOI: 10.21105/joss.05994.

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