Expanding photovoltaic (PV) resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape

aligning with the “carbon peaking and carbon neutrality” objectives. However

rural power grids often lack digitalization; thus

the load distribution within these areas is not fully known. This hinders the calculation of the available PV capacity and deduction of node voltages. This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas. First

houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model. The distribution of the houses is then used to estimate the load distribution in the grid area. Next

equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines. Finally

by calculating the connectivity matrix of the nodes

a minimum spanning tree is extracted

the topology of the network is constructed

and the node parameters of the load-distribution model are calculated. The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas. The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters

thereby offering vital support for determining PV access capability.