Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland

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dc.authoridsahin, mehmet/0000-0001-7942-9253
dc.authoridDeo, Ravinesh/0000-0002-2290-6749
dc.contributor.authorDeo, Ravinesh C.
dc.contributor.authorSahin, Mehmet
dc.date.accessioned2024-12-24T19:27:43Z
dc.date.available2024-12-24T19:27:43Z
dc.date.issued2017
dc.departmentSiirt Üniversitesi
dc.description.abstractForecasting solar radiation (G) is extremely crucial for engineering applications (e.g. design of solar furnaces and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites, instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature(LST) as an effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012-2014 are acquired in seven groups, with three sites per group where the data for first two (2012-2013) are utilised for model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm, and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results, the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899 (MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have an appropriate satellite footprint.
dc.description.sponsorshipUSQ Academic Division
dc.description.sponsorshipThe paper utilized SILO data from the Department of Environment and Resource Management and the MODIS satellite data from National Aeronautics and Space Administration that are duly acknowledged. USQ Academic Division (2016) funded Dr R C Deo through Academic Development and Outside Studies Program (ADOSP, July 2016 - January 2017) to collaborate with Dr M Sahin (Siirt University, Turkey). Special acknowledgement to both reviewers whose comments have improved the final manuscript.
dc.identifier.doi10.1016/j.rser.2017.01.114
dc.identifier.endpage848
dc.identifier.issn1364-0321
dc.identifier.issn1879-0690
dc.identifier.scopus2-s2.0-85010288568
dc.identifier.scopusqualityQ1
dc.identifier.startpage828
dc.identifier.urihttps://doi.org/10.1016/j.rser.2017.01.114
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6763
dc.identifier.volume72
dc.identifier.wosWOS:000400227200064
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofRenewable & Sustainable Energy Reviews
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectSatellite-based solar model
dc.subjectNeural network
dc.subjectMulti-linear regression
dc.subjectARIMA model
dc.titleForecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland
dc.typeReview Article

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