Dust Impact on Photovoltaic Modules: Global Data, Predictive Models, Emphasis on Chemical Composition

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aut.relation.articlenumber100764
aut.relation.endpage100764
aut.relation.journalEnergy Conversion and Management: X
aut.relation.startpage100764
dc.contributor.authorAlmukhtar, Hussam
dc.contributor.authorTjing Lie, Tek
dc.contributor.authorAl-Shohani, Wisam
dc.date.accessioned2024-10-30T00:32:20Z
dc.date.available2024-10-30T00:32:20Z
dc.date.issued2024-10
dc.description.abstractThis study explores the influence of dust on optical properties such as transmittance, absorptance, and emissivity of photovoltaic (PV) modules using over 300 experimental readings from various dust types. These readings were collected during regional storms and ground sources, data encompass different weight levels. Incorporating 690 global datasets and leveraging Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) in MATLAB, the study integrates key dust chemical components (Si, Fe, Ca, Al) and weight to predict the PV optical properties. This approach enhances models’ predictive accuracy across diverse environmental settings, which in turn enables more accurate forecasting of PV power output and thermal behavior under varying dust conditions, as these optical properties govern the module equations. Additionally, comparative analysis with existing literature shows superior accuracy, achieving Mean Squared Errors (MSEs) of 1.8 and 8.44, surpassing previous benchmarks. Results underscore the global efficacy of our methodologies in revealing dust’s impact on PV module thermal behaviour and efficiency.
dc.identifier.citationEnergy Conversion and Management: X, ISSN: 2590-1745 (Print), Elsevier BV, 100764-100764. doi: 10.1016/j.ecmx.2024.100764
dc.identifier.doi10.1016/j.ecmx.2024.100764
dc.identifier.issn2590-1745
dc.identifier.urihttp://hdl.handle.net/10292/18205
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2590174524002423
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDust Impact on Photovoltaic Modules: Global Data, Predictive Models, Emphasis on Chemical Composition
dc.typeJournal Article
pubs.elements-id573233
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