Zhecheng Wang

All Publications

• Exploring the potential of non-residential solar to tackle energy injustice NATURE ENERGY Wussow, M., Zanocco, C., Wang, Z., Prabha, R., Flora, J., Neumann, D., Majumdar, A., Rajagopal, R. 2024

  View details for DOI 10.1038/s41560-024-01485-y

  View details for Web of Science ID 001194849700001

• Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data. Nature communications Wang, Z., Majumdar, A., Rajagopal, R. 2023; 14 (1): 5006

  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.

  View details for DOI 10.1038/s41467-023-39647-3

  View details for PubMedID 37591846

• Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid NATURE ENERGY Wang, Z., Wara, M., Majumdar, A., Rajagopal, R. 2023

  View details for DOI 10.1038/s41560-023-01306-8

  View details for Web of Science ID 001044206100003

• DeepSolar++: Understanding residential solar adoption trajectories with computer vision and technology diffusion models JOULE Wang, Z., Arlt, M., Zanocco, C., Majumadaar, A., Rajagopal, R. 2022; 6 (11): 2611-2625

  View details for DOI 10.1016/j.joule.2022.09.011

  View details for Web of Science ID 000901852600015

• <p>3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D</p> APPLIED ENERGY Mayer, K., Rausch, B., Arlt, M., Gust, G., Wang, Z., Neumann, D., Rajagopal, R. 2022; 310

  View details for DOI 10.1016/j.apenergy.2021.118469

  View details for Web of Science ID 000774189600007

• DeepSolar for Germany: A deep learning framework for PV system mapping from aerial imagery Mayer, K., Wang, Z., Arlt, M., Neumann, D., Rajagopal, R., IEEE IEEE. 2020

  View details for Web of Science ID 000722591200040

• Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding Wang, Z., Li, H., Rajagopal, R., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 1013-1020

  View details for Web of Science ID 000667722801011

• DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States JOULE Yu, J., Wang, Z., Majumdar, A., Rajagopal, R. 2018; 2 (12): 2605–17

  View details for DOI 10.1016/j.joule.2018.11.021

  View details for Web of Science ID 000453896100014