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

Zhecheng Wang; Arun Majumdar; Ram Rajagopal

doi:10.1038/s41467-023-39647-3

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

Nat Commun. 2023 Aug 17;14(1):5006. doi: 10.1038/s41467-023-39647-3.

Authors

Zhecheng Wang^{1

2}, Arun Majumdar^{3

4}, Ram Rajagopal^{5

6}

Affiliations

¹ Department of Civil & Environmental Engineering, Stanford University, Stanford, CA, 94305, USA.
² Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
³ Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA. amajumdar@stanford.edu.
⁴ Department of Energy Science & Engineering, Stanford University, Stanford, CA, 94305, USA. amajumdar@stanford.edu.
⁵ Department of Civil & Environmental Engineering, Stanford University, Stanford, CA, 94305, USA. ramr@stanford.edu.
⁶ Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA. ramr@stanford.edu.

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 same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R² of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

Abstract

Grants and funding