Journal of Solar Energy Research

A Review on Modeling and Prediction of Soiling on Solar Photovoltaics and Thermal Collectors

Document Type : Review Article

Authors

1 Department of Mechanical, Bioresources and Biomedical Engineering, University of South Africa, Science Campus, Florida South Africa

2 Department of Mechanical, Bioresources and Biomedical Engineering, University of South Africa, Florida 1710, South Africa

10.22059/jser.2025.392093.1544

Abstract

Soiling of solar photovoltaic and thermal collectors can significantly reduce energy output, with losses reaching up to 78% in total yield and more than 1% per day in arid regions. Accurate soiling prediction is essential for optimizing system performance, minimizing downtime, and reducing operational expenses. This review critically examines empirical, analytical, and machine learning-based models used to forecast soiling effects. Empirical models, including transmittance loss and particulate matter-based approaches, report errors between <2% and 14%, while regression models show higher inaccuracies ranging from 40% to 93%. Analytical models such as the Bergin and Toth frameworks provide structured physical estimations but often require calibration and may overestimate under certain conditions. Machine learning and deep learning models demonstrate superior predictive performance, with image-based approaches achieving F1 score of 0.913 and models integrating environmental and image data reaching up to 97% accuracy. Despite these advancements, challenges remain, including limited availability of high-quality data, lack of generalizability across different climates, and insufficient real-time adaptability. This review also explores soiling mitigation strategies such as self-cleaning coatings, automated cleaning systems, and environmental monitoring tools. It emphasizes the need for hybrid, adaptive frameworks integrating artificial intelligence and Internet of Things technologies for improved accuracy and operational efficiency.

Keywords

  1. Sharma, S., Malik, P., & Sinha, S. (2024). The impact of soiling on temperature and sustainable solar PV power generation: A detailed analysis. Renewable Energy, 237, 121864. https://doi.org/10.1016/j.renene.2024.121864
  2. Anderson, C. B., Picotti, G., Cholette, M. E., Leslie, B., Steinberg, T. A., & Manzolini, G. (2023). Heliostat-field soiling predictions and cleaning resource optimization for solar tower plants. Applied Energy, 352, 121963. https://doi.org/10.1016/j.apenergy.2023.121963
  3. Fotsing Metegam, I. F., Wolff, E., Tchinda, R., Huart, M., & Chara-Dackou, V. S. (2025). Evaluation of On-Grid and Off-Grid Solar photovoltaic Sites in Cameroon Using Geographic Information Systems, Fuzzy Logic, and Multi-Criteria Analysis. Energy, 134614. https://doi.org/10.1016/j.energy.2025.134614
  4. Chen, J., Chen, K., Zhang, W., Su, J. M., Zhao, B., Hu, M., & Pei, G. (2025). Numerical study of a solar district heating system with photovoltaic-thermal collectors and pit thermal energy storage. Energy, 317, 134705. https://doi.org/10.1016/j.energy.2025.134705
  5. Yakubu, S., Samikannu, R., Gawusu, S., Wetajega, S. D., Okai, V., Shaibu, A.-K. S., & Workneh, G. A. (2025). A holistic review of the effects of dust buildup on solar photovoltaic panel efficiency. Solar Compass, 13, 100101. https://doi.org/10.1016/j.solcom.2024.100101
  6. Kayri, İ., & Bayar, M. T. (2024). A new approach to determine the long-term effect of efficiency losses due to different dust types accumulation on PV modules with artificial neural networks. Journal of Cleaner Production, 434, 140282. https://doi.org/10.1016/j.jclepro. 2023.140282
  7. Alkharusi, T., Alzahrani, M. M., Pandey, C., Yildizhan, H., & Markides, C. N. (2024). Experimental investigation of nonuniform PV soiling. Solar Energy, 272, 112493. https://doi.org/10.1016/j.solener.2024.112493
  8. Tseng, M.-L., Eshaghi, N., Gassoumi, A., Dehkalani, M. M., & Gorji, N. E. (2023). Experimental measurements of soiling impact on current and power output of photovoltaic panels. Modern Physics Letters B, 37(34), 2350182. https://doi.org/10.1142/S0217984923501828
  9. Zereg, K., Gama, A., Aksas, M., Rathore, N., Yettou, F., & Panwar, N. L. (2022). Dust impact on concentrated solar power: A review. Environmental Engineering Research, 27(6). https://doi.org/10.4491/eer.2021.345
  10. Picotti, G., Borghesani, P., Cholette, M. E., & Manzolini, G. (2018). Soiling of solar collectors – Modelling approaches for airborne dust and its interactions with surfaces. Renewable and Sustainable Energy Reviews, 81, 2343–2357. https://doi.org/10.1016/j.rser.2017.06.043
  11. Rashak, Z. M., & Hassan, K. H. (2024). The Impact of Soiling on Photovoltaic Performance in Iraq: Review. Basrah Journal for Engineering Sciences, 24(2). Retrieved from https://www.iasj.net/iasj/article/322630
  12. Laarabi, B., Tevi, G. J., Sinke, W. C., Maiga, A. S., Rajasekar, N., & Barhdadi, A. (2024). Comprehensive literature review on the modeling and prediction of soiling effects on solar energy power plants. Digital Technologies for Solar Photovoltaic Systems, 243–287. https://doi.org/10.1049/PBPO228E_ch9
  13. Ilse, K. K., Figgis, B. W., Naumann, V., Hagendorf, C., & Bagdahn, J. (2018). Fundamentals of soiling processes on photovoltaic modules. Renewable and Sustainable Energy Reviews, 98, 239–254. https://doi.org/10.1016/j.rser.2018.09.015
  14. Smestad, G. P., Germer, T. A., Alrashidi, H., Fernández, E. F., Dey, S., Brahma, H., Sarmah, N., Ghosh, A., Sellami, N., Hassan, I. A. I., Desouky, M., Kasry, A., Pesala, B., Sundaram, S., Almonacid, F., Reddy, K. S., Mallick, T. K., & Micheli, L. (2020). Modelling photovoltaic soiling losses through optical characterization. Scientific Reports, 10(1), 58. https://doi.org/10. 1038/ s41598-019-56868-z
  15. Varga, H. F., & Wiesner, M. R. (2021). Effect of Dust Composition on the Reversibility of Photovoltaic Panel Soiling. Environmental Science & Technology, 55(3), 1984–1991. https://doi.org/10.1021/acs.est.0c06196
  16. Kazmerski, L. L., Diniz, A. S. A. C., Maia, C. B., Viana, M. M., Costa, S. C., Brito, P. P., Campos, C. D., Neto, L. V. M., de Morais Hanriot, S., & de Oliveira Cruz, L. R. (2016). Fundamental Studies of Adhesion of Dust to PV Module Surfaces: Chemical and Physical Relationships at the Microscale. IEEE Journal of Photovoltaics, 6(3), 719–729. Presented at the IEEE Journal of Photovoltaics. https://doi.org/10.1109/JPHOTOV.2016.2528409
  17. Sadat, S. A., Hoex, B., & Pearce, J. M. (2022). A Review of the Effects of Haze on Solar Photovoltaic Performance. Renewable and Sustainable Energy Reviews, 167, 112796. https://doi.org/10.1016/j.rser.2022.112796
  18. Song, Z., Cao, S., & Yang, H. (2024). Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China. Applied Energy, 365, 123261. https://doi.org/10.1016/j.apenergy.2024.123261
  19. Yao, F., & Palmer, P. I. (2022). Source Sector Mitigation of Solar Energy Generation Losses Attributable to Particulate Matter Pollution. Environmental Science & Technology, 56(12), 8619–8628. https://doi.org/10.1021/acs.est.2c01175
  20. Stenchikov, G., Mostamandi, S., Shevchenko, I., Ukhov, A., & Osipov, S. (2023). Coarse Dust Soiling and Fine Dust Dimming Effects on PV Panels Over the Arabian Peninsula. In 2023 Middle East and North Africa Solar Conference (MENA-SC) (pp. 1–3). Presented at the 2023 Middle East and North Africa Solar Conference (MENA-SC). https://doi.org/10.1109/MENA-SC54044.2023.10374528
  21. Sisodia, A. K. (2025). Impact of small particulate deposition on solar photovoltaic (PV) module performance. International Journal of Science and Research Archive, 14(1), 924–929. https://doi.org/10.30574/ijsra.2025.14.1.0036
  22. Elamim, A., Sarikh, S., Hartiti, B., Benazzouz, A., Elhamaoui, S., & Ghennioui, A. (2024). Experimental studies of dust accumulation and its effects on the performance of solar PV systems in Mediterranean climate. Energy Reports, 11, 2346–2359. https://doi.org/10. 1016/j.egyr.2024.01.078
  23. Chala, G. T., Sulaiman, S. A., & Al Alshaikh, S. M. (2024). Effects of Climatic Conditions of Al Seeb in Oman on the Performance of Solar Photovoltaic Panels. Heliyon, e30944. https://doi.org/10.1016/j.heliyon.2024.e30944
  24. Borah, P., Micheli, L., & Sarmah, N. (2023). Analysis of Soiling Loss in Photovoltaic Modules: A Review of the Impact of Atmospheric Parameters, Soil Properties, and Mitigation Approaches. Sustainability, 15(24), 16669. https://doi.org/10.3390/su152416669
  25. Stankov, B., Terziev, A., Vassilev, M., & Ivanov, M. (2024). Influence of Wind and Rainfall on the Performance of a Photovoltaic Module in a Dusty Environment. Energies, 17(14), 3394. https://doi.org/10.3390/en1714 3394
  26. Norde Santos, F., Wilbert, S., Ruiz Donoso, E., El Dik, J., Campos Guzman, L., Hanrieder, N., Fernández García, A., Alonso García, C., Polo, J., Forstinger, A., Affolter, R., & Pitz-Paal, R. (2024). Cleaning of Photovoltaic Modules through Rain: Experimental Study and Modeling Approaches. Solar RRL, 8(24), 2400551. https://doi.org/10.1002/solr.2024005 51
  27. Brahma, H., Pant, S., Micheli, L., Smestad, G. P., & Sarmah, N. (2023). Effect of Environmental Factors on Photovoltaic Soiling: Experimental and Statistical Analysis. Energies, 16(1), 45. https://doi.org/10.3390/en16010045
  28. Yao, W., Kong, X., Xu, A., Xu, P., Wang, Y., & Gao, W. (2023). New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions. Renewable and Sustainable Energy Reviews, 173, 113119. https://doi.org/10.1016/rser.2022.113119
  29. Aïssa, B., Scabbia, G., & Figgis, B. W. (2024). Field Assessment of the DustIQ Optical Soiling Sensor Performance Under the Harsh Conditions of Desert Environments. In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC) (pp. 0024–0026). Presented at the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC). https://doi.org/10.1109/PVSC57443. 2024.10749590
  30. De, S., Shiradkar, N., & Kottantharayil, A. (2024). Spatial Variability of Soiling Loss in Large-Scale PV Installations. In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC) (pp. 0059–0063). Presented at the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC). https://doi.org/10.1109/PVSC57443. 2024.10749529
  31. Fuke, P., De, S., Shiradkar, N., & Kottantharayil, A. (2024). Effect of Soiling on the PV Module Temperature and Soiling Loss Estimation. In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC) (pp. 1328–1331). Presented at the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC). https://doi.org/1109/PVSC57443.2024.10749369
  32. Maghami, M. R., Hizam, H., Gomes, C., Radzi, M. A., Rezadad, M. I., & Hajighorbani, S. (2016). Power loss due to soiling on solar panel: A review. Renewable and Sustainable Energy Reviews, 59, 1307–1316. https://doi.org/1016/j.rser.2016.01.044
  33. Raina, G., & Sinha, S. (2023). Experimental investigations of front and rear side soiling on bifacial PV module under different installations and environmental conditions. Energy for Sustainable Development, 72, 301–313. https://doi.org/10.1016/j.esd.2023.01.001
  34. Paul Ndeto, M., Wafula Wekesa, D., Njoka, F., & Kinyua, R. (2022). Correlating dust deposits with wind speeds and relative humidity to overall performance of crystalline silicon solar cells: An experimental study of Machakos County, Kenya. Solar Energy, 246, 203–215. https://doi.org/10.1016/j.solener.2022.09.050
  35. Etyemezian, V., Nikolich, G., & Gillies, J. A. (2017). Mean flow through utility scale solar facilities and preliminary insights on dust impacts. Journal of Wind Engineering and Industrial Aerodynamics, 162, 45–56. https://doi.org/10.1016/j.jweia.2017.01.001
  36. Ilse, K., Figgis, B., Khan, M. Z., Naumann, V., & Hagendorf, C. (2019). Dew as a Detrimental Influencing Factor for Soiling of PV Modules. IEEE Journal of Photovoltaics, 9(1), 287–294. Presented at the IEEE Journal of Photovoltaics. https://doi.org/10.1109/JPHOTOV.2018.2882649
  37. Chiteka, K., Arora, R., & Sridhara, S. N. (2019). A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models. Energy Systems. https://doi.org/10.1007/s12667-019-00348-w
  38. Jiang, Y., & Lu, L. (2016). Experimentally Investigating the Effect of Temperature Differences in the Particle Deposition Process on Solar Photovoltaic (PV) Modules. Sustainability, 8(11), 1091. https://doi.org/10. 3390/su8111091
  39. Li, F., Yuan, Z., & Wu, W. (2024). Experimental investigation of soiling losses on photovoltaic in high-density urban environments. Applied Energy, 369, 123572. https://doi.org/10.1016/j.apenergy.2024.123572
  40. Alkharusi, T., Huang, G., & Markides, C. N. (2024). Characterisation of soiling on glass surfaces and their impact on optical and solar photovoltaic performance. Renewable Energy, 220, 119422. https://doi.org/10.1016/j.renene. 2023.119422
  41. Bellmann, P., Wolfertstetter, F., Conceição, R., & Silva, H. G. (2020). Comparative modeling of optical soiling losses for CSP and PV energy systems. Solar Energy, 197, 229–237. https://doi.org/10.1016/j.solener.2019.12.045
  42. Hachicha, A. A., Al-Sawafta, I., & Ben Hamadou, D. (2019). Numerical and experimental investigations of dust effect on CSP performance under United Arab Emirates weather conditions. Renewable Energy, 143, 263–276. https://doi.org/10.1016/j.renene. 2019.04.144
  43. Ammari, N., Mehdi, M., Alami Merrouni, A., El Gallassi, H., Chaabelasri, E., & Ghennioui, A. (2022). Experimental study on the impact of soiling on the modules temperature and performance of two different PV technologies under hot arid climate. Heliyon, 8(11), e11395. https://doi.org/10.1016/j.heliyon.2022.e11395
  44. AlZahrani, K. S. (2023). Experimental investigation of soiling impact on PV module performance in Yanbu Al Sinaiyah, Saudi Arabia. Renewable Energy, 216, 119117. https://doi.org/10.1016/j.renene.2023.119117
  45. Sharma, S., Singh, A., & Sinha, S. (2024). Enhancing Techno-Economical Viability of Solar PV Plants through Strategic Cleaning Schedules in Indian Composite Climate Conditions. In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET) (pp. 1–6). Presented at the 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). https://doi. org/10.1109/SEFET61574.2024.10717981
  46. Redondo, M., Platero, C. A., Moset, A., Rodríguez, F., & Donate, V. (2023). Soiling Modelling in Large Grid-Connected PV Plants for Cleaning Optimization. Energies, 16(2), 904. https://doi.org/10.3390/en16020904
  47. Alvarez, D. L., Al-Sumaiti, A. S., & Rivera, S. R. (2020). Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation. IEEE Access, 8, 63832–63839. https://doi.org/1109/ACCESS.2020.2983322
  48. Laarabi, B., Sankarkumar, S., Rajasekar, N., El Baqqal, Y., & Barhdadi, A. (2022). Modeling investigation of soiling effect on solar photovoltaic systems: New findings. Sustainable Energy Technologies and Assessments, 52, 102126. https://doi.org/1016/j.seta.2022.102126
  49. You, S., Lim, Y. J., Dai, Y., & Wang, C.-H. (2018). On the temporal modelling of solar photovoltaic soiling: Energy and economic impacts in seven cities. Applied Energy, 228, 1136–1146. https://doi.org/10.1016/j.apenergy.2018.07.020
  50. Younis, A., & Alhorr, Y. (2021). Modeling of dust soiling effects on solar photovoltaic performance: A review. Solar Energy, 220, 1074–1088. https://doi.org/10.1016/j.solener.2021.04.011
  51. Fregosi, D., & Bolen, M. (2022). An Evaluation of Empirical Models for use in Normalizing PV Plant Performance Data. In 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (pp. 0116–0120). Presented at the 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC). https://doi.org/10.1109/PVSC48317. 2022.9938822
  52. Shakirov, V., Ivanova, I., & Tuguzova, T. (2023). Development of empirical solar radiation models with genetic algorithm and extended validation procedure. International Journal of Green Energy, 20(10), 1101–1118. https://doi.org/10.1080/15435075.2022.2145482
  53. Nwokolo, S. C., Obiwulu, A. U., Amadi, S., & Ogbulezie, J. (2023). Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems. Trends in Renewable Energy. https://doi.org/10.17737/tre.2023.9.2.00156
  54. Dehghan, M., Rashidi, S., & Waqas, A. (2022). Modeling of soiling losses in solar energy systems. Sustainable Energy Technologies and Assessments, 53, 102435. https://doi.org/1016/j.seta.2022.102435
  55. García, M., Marroyo, L., Lorenzo, E., & Pérez, M. (2011). Soiling and other optical losses in solar-tracking PV plants in navarra. Progress in Photovoltaics: Research and Applications, 19(2), 211–217. https://doi.org/10.1002/1004
  56. Boyle, L., Flinchpaugh, H., & Hannigan, M. (2016). Assessment of PM dry deposition on solar energy harvesting systems: Measurement–model comparison. Aerosol Science and Technology, 50(4), 380–391. https://doi.org/1080/02786826.2016.1153797
  57. Conceição, R., Silva, H. G., & Collares-Pereira, M. (2018). CSP mirror soiling characterization and modeling. Solar Energy Materials and Solar Cells, 185, 233–239. https://doi.org/10.1016/solmat.2018.05.035
  58. Sengupta, S., Sengupta, S., & Saha, H. (2020). Comprehensive Modeling of Dust Accumulation on PV Modules Through Dry Deposition Processes. IEEE Journal of Photovoltaics, 10(4), 1148–1157. Presented at the IEEE Journal of Photovoltaics. https://doi.org/10.1109/JPHOTOV.2020.2992352
  59. Bessa, J. G., Micheli, L., Almonacid, F., & Fernández, E. F. (2021). Monitoring photovoltaic soiling: assessment, challenges, and perspectives of current and potential strategies. iScience, 24(3), 102165. https://doi.org/10.1016/j.isci.2021.102165
  60. Haddad, A. G., & Dhaouadi, R. (2018). Modeling and analysis of PV soiling and its effect on the transmittance of solar radiation. In 2018 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1–5). Presented at the 2018 Advances in Science and Engineering Technology International Conferences (ASET). https://doi. org/10.1109/ICASET.2018.8376787
  61. Javed, W., Guo, B., & Figgis, B. (2017). Modeling of photovoltaic soiling loss as a function of environmental variables. Solar Energy, 157, 397–407. https://doi.org/10.1016/j. solener.2017.08.046
  62. Almufarrej, A., & Erfani, T. (2023). Modelling the regional effect of transmittance loss on photovoltaic systems due to dust. International Journal of Energy and Environmental Engineering, 14(3), 379–386. https://doi.org/1007/s40095-022-00510-8
  63. Micheli, L., Caballero, J. A., Fernandez, E. F., Smestad, G. P., Nofuentes, G., Mallick, T. K., & Almonacid, F. (2019). Correlating photovoltaic soiling losses to waveband and single-value transmittance measurements. Energy, 180, 376–386. https://doi.org/1016/j.energy.2019.05.097
  64. Paudyal, B. R., Shakya, S. R., Paudyal, D. P., & Das Mulmi, D. (2017). Soiling-induced transmittance losses in solar PV modules installed in Kathmandu Valley. Renewables: Wind, Water, and Solar, 4(1), 5. https://doi.org/10.1186/s40807-017-0042-z
  65. Elminir, H. K., Ghitas, A. E., Hamid, R. H., El-Hussainy, F., Beheary, M. M., & Abdel-Moneim, K. M. (2006). Effect of dust on the transparent cover of solar collectors. Energy Conversion and Management, 47(18–19), 3192–3203. https://doi.org/10.1016/j.enconman.2006.02.014
  66. Hegazy, A. A. (2001). Effect of dust accumulation on solar transmittance through glass covers of plate-type collectors. Renewable Energy, 22(4), 525–540. https://doi.org/10. 1016/S0960-1481(00)00093-8
  67. Zhou, L., Schwede, D., Appel, K. W., Mangiante, M. J., Wong, D., Napelenok, S., Whung, P., & Zhang, B. (2019). The impact of air pollutant deposition on solar energy system efficiency: An approach to estimate PV soiling effects with the Community Multiscale Air Quality (CMAQ) model. The Science of the total environment, 651 Pt 1, 456–465. https://doi.org/10.1016/j.scitotenv.2018.09.194
  68. Song, Z., Wang, M., & Yang, H. (2022). Quantification of the Impact of Fine Particulate Matter on Solar Energy Resources and Energy Performance of Different Photovoltaic Technologies. ACS Environmental Au, 2(3), 275–286. https://doi.org/10.1021/acsenvironau. 1c00048
  69. Tamoor, M., Hussain, M. I., Bhatti, A. R., Miran, S., Arif, W., Kiren, T., & Lee, G. H. (2022). Investigation of dust pollutants and the impact of suspended particulate matter on the performance of photovoltaic systems. Frontiers in Energy Research, 10. https://doi.org/3389/fenrg.2022.1017293
  70. Coello, M., & Boyle, L. (2019). Simple Model for Predicting Time Series Soiling of Photovoltaic Panels. IEEE Journal of Photovoltaics, 9(5), 1382–1387. Presented at the IEEE Journal of Photovoltaics. https://doi.org/1109/JPHOTOV.2019.2919628
  71. Bessa, J. G., Solas, Á. F., Cruz, F. A., Fernández, E. F., & Micheli, L. (2022). Results of Environmental-Based PV Soiling Models after Extreme Dust Events: The Case of Saharan Dust Intrusions in Southern Spain. In 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (pp. 1294–1294). Presented at the 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC). https://doi.org/10.1109/PVSC48317. 2022.9938938
  72. Lara-Fanego, V., Gueymard, C. A., & Micheli, L. (2023). Soiling Model for PV Applications: Improved Parameterizations. In 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC) (pp. 1–3). Presented at the 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC). https://doi.org/10.1109/PVSC48320.2023.10359694
  73. Guo, B., Javed, W., Figgis, B. W., & Mirza, T. (2015). Effect of dust and weather conditions on photovoltaic performance in Doha, Qatar: 1st Workshop on Smart Grid and Renewable Energy, SGRE 2015. 2015 1st Workshop on Smart Grid and Renewable Energy, SGRE 2015. https://doi.org/10.1109/SGRE.2015.7208718
  74. Guo, B., Javed, W., Khan, S., Figgis, B., & Mirza, T. (2016). Models for Prediction of Soiling-Caused Photovoltaic Power Output Degradation Based on Environmental Variables in Doha, Qatar. Presented at the ASME 2016 10th International Conference on Energy Sustainability collocated with the ASME 2016 Power Conference and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology, American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/ES2016-59390
  75. Kaiss, E.-C. A., & Hassan, N. M. (2023). Numerical Modeling of Dust Deposition Rate on Ground-Mounted Solar Photovoltaic Panels. Journal of Solar Energy Engineering, 145(4), 041003. https://doi.org/10.1115/1.4056217
  76. Picotti, G., Borghesani, P., Manzolini, G., Cholette, M. E., & Wang, R. (2018). Development and experimental validation of a physical model for the soiling of mirrors for CSP industry applications. Solar Energy, 173, 1287–1305. https://doi.org/10.1016/j.solener.2018.08.066
  77. Polo, J., Martín-Chivelet, N., Sanz-Saiz, C., Alonso-Montesinos, J., López, G., Alonso-Abella, M., Battles, F. J., Marzo, A., & Hanrieder, N. (2021). Modeling soiling losses for rooftop PV systems in suburban areas with nearby forest in Madrid. Renewable Energy, 178, 420–428. https://doi.org/10.1016/j.renene. 2021.06.085
  78. Sengupta, S., Chanda, C. K., Saha, H., & Sengupta, S. (2023). Physics based modeling of dust accumulation on a bifacial solar PV module for generation loss estimation due to soiling. Solar Energy Advances, 3, 100046. https://doi.org/10.1016/j.seja.2023.100046
  79. Picotti, G., Cholette, M. E., Anderson, C. B., Steinberg, T. A., & Manzolini, G. (2023). Stochastic soiling loss models for heliostats in Concentrating Solar Power plants. Solar Energy, 263, 111945. https://doi.org/10.1016/j. solener.2023.111945
  80. Gharibzadeh, M., & Massah, M. (2024). Investigation of aerosol deposition using an improved advection-diffusion Eulerian deposition model. E3S Web of Conferences, 575, 03005. https://doi.org/10.1051/e3sconf/ 202457503005
  81. Bergin, M. H., Ghoroi, C., Dixit, D., Schauer, J. J., & Shindell, D. T. (2017). Large Reductions in Solar Energy Production Due to Dust and Particulate Air Pollution. Environmental Science & Technology Letters, 4(8), 339–344. https://doi.org/10.1021/acs.estlett.7b00197
  82. Bessa, J. G., Micheli, L., Montes-Romero, J., Almonacid, F., & Fernández, E. F. (2022). Estimation of Photovoltaic Soiling Using Environmental Parameters: A Comparative Analysis of Existing Models. Advanced Sustainable Systems, 6(5), 2100335. https://doi. org/10.1002/adsu.202100335
  83. Toth, S., Hannigan, M., Vance, M., & Deceglie, M. (2020). Predicting Photovoltaic Soiling From Air Quality Measurements. IEEE Journal of Photovoltaics, 10(4), 1142–1147. Presented at the IEEE Journal of Photovoltaics. https://doi.org/10.1109/JPHOTOV.2020.2983990
  84. Jiang, H., Lu, L., & Sun, K. (2011). Experimental investigation of the impact of airborne dust deposition on the performance of solar photovoltaic (PV) modules. Atmospheric Environment, 45(25), 4299–4304. https://doi.org/10.1016/j.atmosenv.2011.04.084
  85. Sanz Saiz, C., Polo Martínez, J., & Martín Chivelet, N. (2020). Influence of Pollen on Solar Photovoltaic Energy: Literature Review and Experimental Testing with Pollen. Applied Sciences, 10(14), 4733. https://doi.org/10.3390/ app10144733
  86. Yang, H., & Wang, H. (2022). Numerical simulation of the dust particles deposition on solar photovoltaic panels and its effect on power generation efficiency. Renewable Energy, 201, 1111–1126. https://doi.org/10.1016/j.renene.2022.11.043
  87. Zhou, Q., Dong, P., Li, M., & Wang, Z. (2023). Analyzing the interactions between photovoltaic system and its ambient environment using CFD techniques: A review. Energy and Buildings, 296, 113394. https://doi.org/10.1016/j.enbuild. 2023.113394
  88. Raillani, B., Salhi, M., Chaatouf, D., Bria, A., Amraqui, S., & Mezrhab, A. (2023). A new proposed method to mitigate the soiling rate of a photovoltaic array using first-row height. Applied Energy, 331, 120403. https://doi.org/1016/j.apenergy.2022.120403
  89. Chiteka, K., Arora, R., & Jain, V. (2021). CFD Prediction of dust deposition and installation parametric optimisation for soiling mitigation in non-tracking solar PV modules. International Journal of Ambient Energy, 42(11), 1307–1320. https://doi.org/10.1080/01430750.2019.1594373
  90. Peng, H., Lu, H., Chang, X., Zheng, C., & Wang, Y. (2022). 3D CFD modelling of dust deposition characteristics and influences on building-mounted photovoltaic system. Case Studies in Thermal Engineering, 35, 102138. https://doi.org/10.1016/j.csite.2022.102138
  91. Abdolahzadeh, M., Parsa Mofrad, N., & Tayebi, A. (2023). Numerical simulation of dust deposition on rooftop of photovoltaic parking lots supporting electric vehicles charging. Journal of Wind Engineering and Industrial Aerodynamics, 239, 105444. https://doi.org/1016/j.jweia.2023.105444
  92. Al-Gaheeshi, A. M. R., Rashid, F. L., Al-Obaidi, M. A., Hammoodi, K. A., & Agyekum, E. B. (2024). CFD modelling of Darcian flow of water in porous media: Effects of sand grain size. International Journal of Thermofluids, 24, 100990. https://doi.org/10.1016/j.ijft.2024.100990
  93. Shukla, K. N., Rangnekar, S., & Sudhakar, K. (2015). Comparative study of isotropic and anisotropic sky models to estimate solar radiation incident on tilted surface: A case study for Bhopal, India. Energy Reports, 1, 96–103. https://doi.org/10.1016/j.egyr.2015.03.003
  94. Khalid, H. M., Rafique, Z., Muyeen, S. M., Raqeeb, A., Said, Z., Saidur, R., & Sopian, K. (2023). Dust accumulation and aggregation on PV panels: An integrated survey on impacts, mathematical models, cleaning mechanisms, and possible sustainable solution. Solar Energy, 251, 261–285. https://doi.org/10.1016/j.solener. 2023.01.010
  95. Roumpakias, E., & Stamatelos, T. (2020). Surface Dust and Aerosol Effects on the Performance of Grid-Connected Photovoltaic Systems. Sustainability, 12(2), 569. https://doi.org/10.3390/su12020569
  96. Hassan, Q., Jaszczur, M., & Przenzak, E. (2017). Mathematical model for the power generation from arbitrarily oriented photovoltaic panel. E3S Web of Conferences, 14, 01028. https://doi.org/10.1051/e3sconf/20171401028
  97. Fan, S., Wang, Y., Cao, S., Sun, T., & Liu, P. (2021). A novel method for analyzing the effect of dust accumulation on energy efficiency loss in photovoltaic (PV) system. Energy, 234, 121112. https://doi.org/10.1016/j.energy.2021.121112
  98. Al-Addous, M., Dalala, Z., Alawneh, F., & Class, C. B. (2019). Modeling and quantifying dust accumulation impact on PV module performance. Solar Energy, 194, 86–102. https://doi.org/10.1016/j.solener.2019.09.086
  99. Garud, K. S., Jayaraj, S., & Lee, M.-Y. (2021). A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models. International Journal of Energy Research, 45(1), 6–35. https://doi.org/10.1002/er.5608
  100. Tina, G. M., Ventura, C., Ferlito, S., & De Vito, S. (2021). A State-of-Art-Review on Machine-Learning Based Methods for PV. Applied Sciences, 11(16), 7550. https://doi.org/3390/app11167550
  101. Chauhan, R., Sharma, S., & Pachauri, R. (2024). Performance Prediction of Conventional and Modified Solar Stills Using Levenberg Marquardt Algorithm-Based Artificial Neural Network Model: An Experimental and Stochastic Evaluation. Journal of Solar Energy Research, 9(3), 1966–1980. https://doi.org/22059/jser.2024.380006.1449
  102. Barhmi, K., Heynen, C., Golroodbari, S., & van Sark, W. (2024). A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence. Solar, 4(1), 99–135. https://doi.org/10.3390/solar4010005
  103. Ebrie, A. S., & Kim, Y. J. (2024). Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid. Renewable Energy, 230, 120886. https://doi.org/10.1016/j.renene.2024.120886
  104. Srivastava, R. K., & Gupta, A. (2024). Short Term Forecasting of Solar Irradiance Using Ensemble CNN-BiLSTM-MLP Model Combined with Error Minimization and CEEMDAN Pre-Processing Technique. Journal of Solar Energy Research, 9(1), 1763–1779. https://doi.org/10.22059/jser.2024.369290.1363
  105. Zhang, W., Liu, S., Gandhi, O., Rodríguez-Gallegos, C. D., Quan, H., & Srinivasan, D. (2021). Deep-Learning-Based Probabilistic Estimation of Solar PV Soiling Loss. IEEE Transactions on Sustainable Energy, 12, 2436–2444. https://doi.org/10.1109/tste.2021.3098677
  106. Yang, M., Javed, W., Guo, B., & Ji, J. (2024). Estimating PV Soiling Loss Using Panel Images and a Feature-Based Regression Model. IEEE Journal of Photovoltaics, 14(4), 661–668. https://doi.org/10.1109/JPHOTOV.2024.3388168
  107. Chatterjee, S., Khan, P. W., & Byun, Y.-C. (2024). Recent advances and applications of machine learning in the variable renewable energy sector. Energy Reports, 12, 5044–5065. https://doi.org/10.1016/j.egyr.2024.09.073
  108. Kumar, A., Dubey, A. K., Segovia Ramírez, I., Muñoz del Río, A., & García Márquez, F. P. (2024). Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking. Archives of Computational Methods in Engineering, 31(8), 4429–4453. https://doi.org/1007/s11831-024-10125-3
  109. Aouidad, H. I., & Bouhelal, A. (2024). Machine learning-based short-term solar power forecasting: a comparison between regression and classification approaches using extensive Australian dataset. Sustainable Energy Research, 11(1), 28. https://doi.org/10.1186/ s40807-024-00115-1
  110. Lopez-Lorente, J., Polo, J., Martín-Chivelet, N., Norton, M., Livera, A., Makrides, G., & Georghiou, G. E. (2023). Characterizing soiling losses for photovoltaic systems in dry climates: A case study in Cyprus. Solar Energy, 255, 243–256. https://doi.org/10.1016/j.solener.2023.03.034
  111. Fang, M., Qian, W., Qian, T., Bao, Q., Zhang, H., & Qiu, X. (2024). DGImNet: A deep learning model for photovoltaic soiling loss estimation. Applied Energy, 376, 124335. https://doi.org/10.1016/j.apenergy.2024.124335
  112. Suhaimi, M. A. A. M., Dahlan, N. Y., Asman, S. H., Rajasekar, N., & Mohamed, H. (2024). Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition. International Journal of Advances in Applied Sciences, 13(4), 796–805. https://doi.org/11591/ijaas.v13.i4.pp796-805
  113. Sharma, J., Khattar, S., & Verma, T. (2024). Design and Development of Soil Detection Framework From Solar Panel Using Deep Learning. In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 (pp. 1–7). Presented at the 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. https://doi.org/10.1109/OTCON60325.2024.10688333
  114. Rinesh, S., Deepa, S., Nandan, R. T., Sachin, R. S., Thamil, S. V., Akash, R., Arun, M., Prajitha, C., & Kumar, A. P. S. (2024). Prediction and classification of solar photovoltaic power generation using extreme gradient boosting regression model. International Journal of Low-Carbon Technologies, 19, 2420–2430. https://doi.org/1093/ijlct/ctae197
  115. Redekar, A., Dhiman, H. S., Deb, D., & Muyeen, S. M. (2024). On reliability enhancement of solar PV arrays using hybrid SVR for soiling forecasting based on WT and EMD decomposition methods. Ain Shams Engineering Journal, 15(6), 102716. https://doi. org/10.1016/j.asej.2024.102716
  116. Kappler, T., Starosta, A. S., Schwarz, B., Munzke, N., & Hiller, M. (2024). Inclusion of Shading and Soiling With Physical and Data-Driven Algorithms for Solar Power Forecasting. PV-Symposium Proceedings, 1. https://doi.org/52825/pv-symposium.v1i.1063
  117. Zhang, W., Archana, V., Gandhi, O., Rodríguez-Gallegos, C. D., Quan, H., Yang, D., Tan, C.-W., Chung, C. Y., & Srinivasan, D. (2024). SoilingEdge: PV Soiling Power Loss Estimation at the Edge Using Surveillance Cameras. IEEE Transactions on Sustainable Energy, 15(1), 556–566. Presented at the IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2023.3320690
  118. Brenner, A., Kahn, J., Hirsch, T., Röger, M., & Pitz-Paal, R. (2023). Soiling determination for parabolic trough collectors based on operational data analysis and machine learning. Solar Energy, 259, 257–276. https://doi.org/1016/j.solener.2023.05.008
  119. Araujo Costa Silva, L., Baca Ruiz, L. G., Criado-Ramón, D., Gabriel Bessa, J., Micheli, L., & Pegalajar Jiménez, M. del C. (2023). Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques. Expert Systems with Applications: An International Journal, 231(C). https://doi.org/1016/j.eswa.2023.120816
  120. Rezk, M., Chikte, R., Aljasmi, N., & Baloch, A. A. B. (2023). PV Module Classification Based on Spatial Distribution of Soiling Using Deep Learning Model. In 2023 Middle East and North Africa Solar Conference (MENA-SC) (pp. 1–4). Presented at the 2023 Middle East and North Africa Solar Conference (MENA-SC). https://doi.org/10.1109/MENA-SC54044. 2023.10374510
  121. Evstatiev, B. I., Trifonov, D. T., Gabrovska-Evstatieva, K. G., Valov, N. P., & Mihailov, N. P. (2024). PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning. Energies, 17(20), 5238. https:// doi.org/10.3390/en17205238
  122. Mishra, E., Sreejith, S., & Patil, G. H. (2024). Soiling Losses Estimation in Solar Panels Using Different Analytical Methods. In 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC) (pp. 189–194). Presented at the 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC). https://doi.org/10.1109/PARC59193.2024.10486761
  123. Fösel, T., Tighineanu, P., Weiss, T., & Marquardt, F. (2018). Reinforcement Learning with Neural Networks for Quantum Feedback. Physical Review X, 8(3), 031084. https://doi.org/10.1103/PhysRevX.8.031084
  124. Pulipaka, S., Mani, F., & Kumar, R. (2016). Modeling of soiled PV module with neural networks and regression using particle size composition. Solar Energy, 123, 116–126. https://doi.org/10.1016/j.solener.2015.11.012
  125. Mani, F., Pulipaka, S., & Kumar, R. (2015). Modeling of soiled photovoltaic modules with neural networks using particle size composition of soil. In 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC) (pp. 1–4). Presented at the 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC). https://doi.org/1109/PVSC.2015.7355991
  126. Sharma, S., Raina, G., Yadav, S., & Sinha, S. (2023). A comparative evaluation of different PV soiling estimation models using experimental investigations. Energy for Sustainable Development, 73, 280–291. https://doi.org/10.1016/j.esd.2023.02.008
  127. S, S., V, D., P S, R. P., & Subramani, K. (2023). Detection of Soiling on PV Module using Deep Learning. International Journal of Electrical and Electronics Engineering, 10(7), 93–101. https://doi.org/10.14445/23488379/ IJEEE-V10I7P108
  128. Muller, M., & Rashed, F. (2023). Considering the Variability of Soiling in Long-Term PV Performance Forecasting. IEEE Journal of Photovoltaics, 13(6), 825–829. Presented at the IEEE Journal of Photovoltaics. https://doi.org/1109/JPHOTOV.2023.3300369
  129. Voukelatos, A., Anastasiou, A., Sattler, J. C., Alexopoulos, S., Dutta, S., & Kioutsioukis, I. (2022). Design and Implementation of a Soiling Forecasting Tool for Parabolic Through Collector Mirrors. SolarPACES Conference Proceedings, 1. https://doi.org/10.52825/solarpaces.v1i.722
  130. Al Arni, S., Mahdi, J. M., Abed, A. M., Hammoodi, K. A., Hasan, H. A., Homod, R. Z., & Khedher, N. B. (2024). Novel multi-layer nano-modified PCM configuration for efficient thermal management of photovoltaic-thermal systems. Journal of Energy Storage, 103, 114352. https://doi.org/10.1016/j.est.2024.114352
  131. Jasim, A. K., Alwan, S. H., Nemah, A. K., Hussein, H. Q., & Hammoodi, K. A. (2024). A Numerical Study to Improve Heat Transfer in a Rectangular Cell Filled with Phase Change Materials Using Several Types of Rods. International Journal of Heat and Technology, 42(6), 2108–2114. https://doi.org/10.18280/420629
  132. Hashemian, N., & Noorpoor, A. (2019). Assessment and multi-criteria optimization of a solar and biomass-based multi-generation system: Thermodynamic, exergoeconomic and exergoenvironmental aspects. Energy Conversion and Management, 195, 788–797. https://doi.org/10.1016/j.enconman.2019.05.039
  133. Ilse, K., Micheli, L., Figgis, B. W., Lange, K., Daßler, D., Hanifi, H., Wolfertstetter, F., Naumann, V., Hagendorf, C., Gottschalg, R., & Bagdahn, J. (2019). Techno-Economic Assessment of Soiling Losses and Mitigation Strategies for Solar Power Generation. Joule, 3(10), 2303–2321. https://doi.org/10.1016/j. joule.2019.08.019
  134. Baras, A., Jones, R. K., Alqahtani, A., & Alodan, M. (2016). Measured soiling loss and its economic impact for PV plants in central Saudi Arabia. In 2016 Saudi Arabia Smart Grid (SASG) (pp. 1–7). Presented at the 2016 Saudi Arabia Smart Grid (SASG). https://doi.org/1109/SASG.2016.7849657
  135. Micheli, L., Fernández, E. F., & Almonacid, F. (2021). Tracking Soiling Losses and Cleaning Profits Trends. In 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC) (pp. 0144–0146). Presented at the 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). https://doi.org/10.1109/PVSC43889. 2021.9518866
  136. Jung, D., Gareis, G. H., Staiger, A., & Salmon, A. (2022). Effects of soiling on agrivoltaic systems: Results of a case study in Chile. AIP Conference Proceedings, 2635(1), 020001. https://doi.org/10.1063/5.0107943
  137. Montecchi, M., & Sutter, F. (2023). Soiling model for spectral reflectance of solar mirrors. Solar Energy, 259, 356–363. https://doi.org/1016/j.solener.2023.05.017
Journal of Solar Energy Research
Volume 10, Issue 1
January 2025
Pages 2135-2160