Work

A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images

Year: 2025

Type: article

Abstract: Dust is one of the key factors influencing photovoltaic (PV) power generation. The ability to accurately capture PV dust information is essential for PV operation and sustainable utilization. This study employs uncrewed aerial vehicle (UAV) hyperspectral imaging to monitor PV dust deposition. To address the problems of information redundancy in hyperspectral data and the backpropagation neural network (BPNN) easily falling into local optimum, a high-precision UAV hyperspectral PV dust estimation method is proposed. The fractional order derivative (FOD) is applied to the spectral reflectance of PV dust accumulation, and a PV dust estimation model with sine map tuna swarm optimized backpropagation neural network (STSO-BPNN) is established, which is validated using UAV hyperspectral images and ground measured dust data. The results show that FOD improves the spectral signal-to-noise ratio, and the 0.2 order STSO-BPNN model achieves higher accuracy (R 2 = 0.95, RMSE = 0.79 g/m 2 , RPIQ = 7.98). These findings provide a scientific basis for the rapid and accurate estimation and mapping of PV dust accumulation while proposing a novel strategy for efficient PV implementation and management.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104500

Authors: Peng Zhu, Hao Li, Pan Zheng

Institution: Hohai University

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,550