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dc.contributor.authorPan, I
dc.contributor.authorPandey, DS
dc.contributor.authorDas, S
dc.date.accessioned2018-01-19T10:08:49Z
dc.date.issued2013-12
dc.description.abstractIn this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those, obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach.en_GB
dc.identifier.citationVol. 5, article 063129en_GB
dc.identifier.doi10.1063/1.4850495
dc.identifier.urihttp://hdl.handle.net/10871/31091
dc.language.isoenen_GB
dc.publisherAIP Publishingen_GB
dc.rights© 2013 AIP Publishing LLC.en_GB
dc.subjectSolar radiationen_GB
dc.subjectArtificial neural networksen_GB
dc.subjectSolar energyen_GB
dc.subjectNumerical solutionsen_GB
dc.subjectResearchersen_GB
dc.titleGlobal solar irradiation prediction using a multi-gene genetic programming approachen_GB
dc.typeArticleen_GB
dc.date.available2018-01-19T10:08:49Z
dc.descriptionThis is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.en_GB
dc.identifier.journalJournal of Renewable and Sustainable Energyen_GB


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