Estimation of the Power Loss of a Soiled Photovoltaic Panel Using Image Analysis Techniques

Francois Brunel, Ricardo López, Florencio García, Eduardo Peters and Gustavo Funes ()
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Francois Brunel: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile
Ricardo López: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile
Florencio García: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile
Eduardo Peters: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile
Gustavo Funes: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile

Energies, 2025, vol. 18, issue 18, 1-16

Abstract: Soiling is one of the main problems of photovoltaic power. It is estimated that some areas could accumulate up to 0.6 % of soil per day. This, along with the lack of rainfall in arid zones, produces a considerable energy loss. Soil detection has been studied previously in the literature using artificial intelligence methods that require an extensive amount of images to train. Here, we propose an algorithmic approach that focuses on the characteristics of the images to discriminate different levels of soiling. Our method requires the construction of a soiling simulator to deposit layers of soil over a module while measuring the electric variables. From the datasets obtained, a calibration vector is established, which allows for the estimation of the power produced by the soiled panel from a captured image of it. We have found that the maximum error is approximately 3 % when applying the model to images of its own dataset. The error then varies from 3 % to 10 % when determining power from another dataset and up to 10 % when applying the model to an external dataset. We believe this work is a pioneer in the estimation of power produced by a soiled panel by examining only a picture.

Keywords: dust detection; photovoltaic; computer vision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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