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Nt1310 Unit 3 Ann Architecture

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3.1 First ANN architecture design The first ANN architecture design adopts the PV module temperature (TCell) in °Celsiusand the irradiance (E) in W/m2, as the inputs of the network and the PV generator output power (P) in Watt, as the output of the network. The data set are trained until new patterns may be presented to them forprediction or classification. Our purpose is to see the behaviour of the new obtained power values, compared to recorded experimental data with respect to the data set. 3.2 SecondANN architecture design Similar to the previous work [17], the second ANN architecture design adopts the PV module short circuit(ISC) in Ampere,and the open circuit voltage (VOC) in Volts, as the inputs of the network and the PV generator output power (P) in Watt, as the output of the network. The data set stored in the data acquisition is trained until new patterns may be presented to them forprediction. Thus the trained data are compared to the experimental measurements, recorded during the characterization tests. 3.3 Third ANN architecture design The third ANN architecture design adopts four inputs, as follows: The PV …show more content…

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

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