An Artificial Intelligence‐Based Framework to Accelerate Data‐Driven Policies to Promote Solar Photovoltaics in Lisbon

Due to the unavailability of up‐to‐date and georeferenced information about Lisbon's existing solar energy systems, tracking the progress of solar energy in relation to the city's Climate Action Plans 2030 is a complex task, thus hindering the potential of data‐driven decision‐making for a targeted implementation of photovoltaics (PV) in buildings and urban infrastructure. To overcome the challenges posed, an integrated approach to accelerate policy‐making based on artificial intelligence (AI) resources and local citizens' and stakeholders' participation is developed and piloted in Lisbon. Recurring to a two‐step AI model setup to identify and geolocate PV systems, key policy indicators are calculated to inform policy‐makers about the evolution of PV deployment in the city and contribute to tailor future incentives to more depressed or energy poor districts. The AI model based on open data orthophotos from 2016 allowed estimates for the installed peak power at the city level, in that year, to be delivered in a few minutes, whereas manual inspection of aerial images will have taken several months. Although the PV capacity determined is 30% lower than the historical official numbers, the proof of concept for the proposed framework is achieved and validated by local stakeholders. Lisbon is the third sunniest European capital; However, its photovoltaics (PVs) capacity is still slightly above 10 MWp. For policy‐making and planning of distributed PV in the city, it is paramount to have disaggregated info about peak power and geolocation, which are publicly inaccessible. In this work, aerial images are coupled with artificial intelligence to construct an alternative mapping tool.