Investigating the suitability of GIS and remotely-sensed datasets for photovoltaic modelling on building rooftops

Rising energy demands and net-zero targets have led to development and implementation of renewable technologies. Rooftop photovoltaic (PV) solar panels offer a viable solution while minimising complex construction or excessive infrastructure development. However, critical physical factors influence building suitability to maximise cost-benefit requirements. This research adopted a Geographic Information System (GIS) approach to identify building suitability by analysing and comparing Digital Surface Models (DSM) derived from Light Detection and Ranging (LiDAR) and orthophotography data using UK standardised PV formulas. Geospatial workflows processed rooftop features and modelled outputs for solar irradiation, panel type, kWh, CO2, payback and costs while 3D models and solar web applications were used to validate results. Both models suggested a range of between 14.2 and 15.2 GWh potential for an installed capacity of between 21.1 and 22.3 MW. Residential models met between 62.1% (LiDAR) and 66.6% (orthophotography) of average consumer demand with PV potential exceeding 94% of residential dwellings. LiDAR and orthophotography models had strong agreement with existing PV installations. The methodology can be scaled to a regional level and expanded for larger PV capacity. Moreover, the process can assist policymakers with informed decisions on renewable technologies alongside developments such as Peer to Peer (P2P) solar trading.