Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data.

ABSTRACT: Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the sa

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10435496 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature