Publication:
Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia?s meteorological condition

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dc.citedby0
dc.contributor.authorSuhaimi M.A.A.M.en_US
dc.contributor.authorDahlan N.Y.en_US
dc.contributor.authorAsman S.H.en_US
dc.contributor.authorRajasekar N.en_US
dc.contributor.authorMohamed H.en_US
dc.contributor.authorid57553630500en_US
dc.contributor.authorid24483200900en_US
dc.contributor.authorid57194493395en_US
dc.contributor.authorid35090434600en_US
dc.contributor.authorid57136356100en_US
dc.date.accessioned2025-03-03T07:41:26Z
dc.date.available2025-03-03T07:41:26Z
dc.date.issued2024
dc.description.abstractSolar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems. ? 2024, Intelektual Pustaka Media Utama. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijaas.v13.i4.pp796-805
dc.identifier.epage805
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85210073357
dc.identifier.spage796
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85210073357&doi=10.11591%2fijaas.v13.i4.pp796-805&partnerID=40&md5=2f878946accbc29e2b54d86840bbc0eb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36141
dc.identifier.volume13
dc.pagecount9
dc.publisherIntelektual Pustaka Media Utamaen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Advances in Applied Sciences
dc.titleModelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia?s meteorological conditionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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