Speaker
Description
The growing demand for sustainable energy solutions has accelerated the deployment of renewable energy infrastructure across Africa, particularly wind and solar photovoltaic (PV) systems. However, comprehensive and accessible datasets describing the spatial distribution and generation capacity of such infrastructure remain limited. This study addresses this gap by leveraging openly available geospatial data from OpenStreetMap (OSM) to extract and analyse information on wind and solar power installations in selected African countries. Using data obtained from the Geofabrik repository, relevant features associated with renewable energy systems were filtered and processed to identify infrastructure locations and attributes.
A data-driven geospatial methodology was employed, incorporating tools such as esy-osmfilter and Python libraries such as pandas, geopandas, and shapely for data extraction, cleaning, and analysis. Generation capacity values were standardised and distributed across related spatial elements to improve data consistency and reliability. The resulting datasets were used to assess the spatial distribution and estimated capacity of renewable energy infrastructure. The findings demonstrate the potential of OSM as a valuable, low-cost data source for energy system analysis and planning, particularly in data-scarce regions. This approach provides a scalable framework for supporting renewable energy policy development, infrastructure planning, and future integration studies in Africa.
| Apply for student award at which level: | MSc |
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| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |