Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach

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dc.authoridDeo, Ravinesh/0000-0002-2290-6749
dc.contributor.authorDeo, Ravinesh C.
dc.contributor.authorSahin, Mehmet
dc.contributor.authorAdamowski, Jan F.
dc.contributor.authorMi, Jianchun
dc.date.accessioned2024-12-24T19:27:44Z
dc.date.available2024-12-24T19:27:44Z
dc.date.issued2019
dc.departmentSiirt Üniversitesi
dc.description.abstractGlobal advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555-0.896 vs. 0.411-0.858 (RF), 0.434-0.811 (M5 Tree), and 0.113-0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715-7.191% vs. 4.907-10.784% (RF), 7.111-11.169% (M5 Tree) and 4.591-18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint.
dc.identifier.doi10.1016/j.rser.2019.01.009
dc.identifier.endpage261
dc.identifier.issn1364-0321
dc.identifier.issn1879-0690
dc.identifier.scopus2-s2.0-85060228798
dc.identifier.scopusqualityQ1
dc.identifier.startpage235
dc.identifier.urihttps://doi.org/10.1016/j.rser.2019.01.009
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6764
dc.identifier.volume104
dc.identifier.wosWOS:000458294500018
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/closedAccess
dc.snmzKA_20241222
dc.subjectSatellite solar model
dc.subjectRemote sensing
dc.subjectExtreme learning machine
dc.subjectSpatial forecasting
dc.titleUniversally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach
dc.typeReview Article

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