- Prediction of grid-connected photovoltaic performance using artificial neural networks and experimental dataset considering environmental variation

a ANN environment, b MLP, SOFM and SVM architectures, c GCPV modelling, performance prediction, and results validation methodology

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Prediction of grid-connected photovoltaic performance using artificial neural networks and experimental dataset considering environmental variation - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/a-ANN-environment-b-MLP-SOFM-and-SVM-architectures-c-GCPV-modelling-performance_fig1_358654219 [accessed 29 Mar, 2023]

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a ANN environment, b MLP, SOFM and SVM architectures, c GCPV modelling, performance prediction, and results validation methodology

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<a href="https://www.researchgate.net/figure/a-ANN-environment-b-MLP-SOFM-and-SVM-architectures-c-GCPV-modelling-performance_fig1_358654219"><img src="https://www.researchgate.net/publication/358654219/figure/fig1/AS:11431281120896165@1676689472123/a-ANN-environment-b-MLP-SOFM-and-SVM-architectures-c-GCPV-modelling-performance.png" alt="a ANN environment, b MLP, SOFM and SVM architectures, c GCPV modelling, performance prediction, and results validation methodology"/></a>