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.