Electrical engineering

Meteorology

Photovoltaics

Building-integrated photovoltaics

Operating system

Software deployment

Civil engineering

Engineering

Systems engineering

Geography

Electricity

Roof

Scale (ratio)

Computer science

Photovoltaic system

Cartography

Environmental science

Geographic information system

Remote sensing

Estimation

Applied Energy

The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24±9TWh. This could meet more than 40% of Switzerland's current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.

Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty