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May 9, 2026Next Energy0 citationsOpen Access

Quantitative assessment of fly ash–induced soiling in photovoltaics: Experimental validation and predictive modelling

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KFK. FadilKKK. KandoussiROR. El Otmani

Key Points

  • This study aims to quantify the effects of fly ash accumulation on photovoltaic module performance.
  • Combined experimental and numerical approach to assess fly ash-induced soiling impact.
  • Measured PV performance metrics with strong correlation against simulated results.
  • Utilized predictive modelling to validate the effects of fly ash on optical and thermal parameters.
  • Achieved a Pearson correlation coefficient of 0.997 and coefficient of determination of 0.994, indicating high agreement.
  • Low error metrics demonstrated (RMSE = 0.79 °C, MAE = 0.62 °C) bolstering model's predictive accuracy.
  • Findings enhance understanding of degradation mechanisms, aiding design of mitigation strategies for PV deployment.

Abstract

In urban areas with intense industrial, vehicular, and residential activity, airborne fly ash particles generated by combustion processes constitute a critical factor in photovoltaic (PV) performance degradation. Their deposition on PV glass surfaces reduces optical transmittance, alters photon scattering, and raises cell operating temperature, ultimately lowering conversion efficiency. This study quantifies the impact of fly ash accumulation on PV modules using a combined experimental and numerical approach. A strong consistency is observed between measured and simulated results, with a Pearson correlation coefficient of 0.997 and a coefficient of determination of 0.994, indicating excellent linear agreement. The predictive performance of the model is further supported by low error metrics (Root Mean Square Error RMSE = 0.79 °C, MAE = 0.62 °C). The originality of this work lies in coupling experimental validation with numerical modelling under fly ash exposure, providing a quantitative framework for assessing soiling-induced thermal and optical effects. The findings not only enhance the understanding of degradation mechanisms in polluted environments but also inform the design of mitigation and preventive strategies for PV deployment in regions with high particulate emissions.

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Cite This Study

Fadil et al. (2026) studied this question.

synapsesocial.com/papers/69fecf16b9154b0b82876202https://doi.org/10.1016/j.nxener.2026.100642
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