Urban-scale solar irradiance and photovoltaic (PV) potential datasets at building footprint resolution provide a critical foundation for assessing the feasibility of distributed energy resources in Australian cities. This dataset integrates spatial, meteorological and building footprint data to enable users to query and aggregate rooftop solar potential at the scale of individual buildings. By aligning geospatial mapping with temporal energy modelling, the dataset supports robust analysis of rooftop PV generation across spatial and temporal dimensions. The methodology has been demonstrated through a case study of the City of Kalgoorlie-Boulder in Western Australia, a regional centre with a population of approximately 30,000, situated on the traditional lands of the Wangkatja peoples.
The datasets are generated across three temporal dimensions: hourly, daily and monthly. Hourly calculations of solar irradiance provide fine-grained data that can be aggregated into daily averages and cumulative monthly values. This structure enables exploration of short-term fluctuations, medium-term averages and long-term patterns in solar energy potential. The geographic coverage includes 530 mesh blocks and nearly 16,000 building footprints across Kalgoorlie, Boulder and Trafalgar, reflecting both the urban morphology and heritage character of the city. With over 300 days of sunshine annually, the region offers high solar irradiance levels that make it a promising site for rooftop PV deployment.
The rationale for creating the dataset stems from the need to reduce the carbon footprint of the built environment, which currently accounts for around 40 percent of global energy use and a third of greenhouse gas emissions. Rooftop PV systems represent one of the most accessible and scalable clean energy solutions for cities, yet their deployment is constrained by the variability of solar irradiance and the complexities of urban form. In Kalgoorlie-Boulder, the combination of a semi-arid climate, a historic urban layout conducive to distributed energy systems and a strong civic commitment to sustainability makes the city an ideal testbed for developing and validating PV potential datasets.
The datasets were generated using a computational framework that integrates open geospatial data sources, long-term climate datasets and solar simulation tools. Inputs include building footprint data from Microsoft Bing, climate data from CSIRO and
climate.onebuilding.org, and irradiance calculations using the LBNL Radiance Suite. While the methodology ensures internal consistency and has been validated through urban PV potential use cases, limitations include the use of synthetic climate files, simplified assumptions regarding roof tilt and orientation, and the exclusion of overshadowing effects from trees, terrain or adjacent structures.
The datasets are designed for a wide audience of users. Local governments, energy utilities, planners and regulators can apply the data to support energy policy, infrastructure planning and the integration of distributed energy resources into urban systems. Building owners, property managers and consultants can utilise the data for feasibility studies, system sizing and installation planning. Academic researchers and students can adapt the methodology to extend rooftop solar analyses to other towns and regions across Australia. By providing spatially explicit, temporally detailed and openly accessible data, this project advances the evidence base required for accelerating the transition to clean energy in urban settings.
