A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images
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 add...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001475 |
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| author | Peng Zhu Hao Li Pan Zheng |
| author_facet | Peng Zhu Hao Li Pan Zheng |
| author_sort | Peng Zhu |
| collection | DOAJ |
| description | 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 (R2 = 0.95, RMSE = 0.79 g/m2, 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. |
| format | Article |
| id | doaj-art-5eccafc4dd78464ea533b13e95651598 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-5eccafc4dd78464ea533b13e956515982025-08-20T02:58:25ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910450010.1016/j.jag.2025.104500A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral imagesPeng Zhu0Hao Li1Pan Zheng2School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaCorresponding author at: School of Earth Sciences and Engineering, Hohai University, No. 8 Fochengxi Road, Nanjing, Jiangsu 211100, China.; School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaDust 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 (R2 = 0.95, RMSE = 0.79 g/m2, 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.http://www.sciencedirect.com/science/article/pii/S1569843225001475UAV hyperspectral imageryPV dustFractional order derivativeMachine learning |
| spellingShingle | Peng Zhu Hao Li Pan Zheng A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images International Journal of Applied Earth Observations and Geoinformation UAV hyperspectral imagery PV dust Fractional order derivative Machine learning |
| title | A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images |
| title_full | A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images |
| title_fullStr | A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images |
| title_full_unstemmed | A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images |
| title_short | A hybrid framework for estimating photovoltaic dust content based on UAV hyperspectral images |
| title_sort | hybrid framework for estimating photovoltaic dust content based on uav hyperspectral images |
| topic | UAV hyperspectral imagery PV dust Fractional order derivative Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001475 |
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