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• W2051251128 abstract "The paper deals with the forecast of the power production for three different PhotoVoltaic (PV) plants using an on-line self learning prediction algorithm. The plants are located in Italy at different latitudes. This learning algorithm is based on a radial basis function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. Its on-line learning mechanism gives the chance to avoid the initial training of the NN with a large data set. The performances of the algorithm are tested on the three PV plants with different peak power, panel's materials, orientation and tilting angle. Results are compared to a classical RBF neural network." @default.
• W2051251128 created "2016-06-24" @default.
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• W2051251128 date "2012-06-01" @default.
• W2051251128 modified "2023-10-07" @default.
• W2051251128 title "Online tuned neural networks for PV plant production forecasting" @default.
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• W2051251128 doi "https://doi.org/10.1109/pvsc.2012.6318197" @default.
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