Abstract:
The MEA-wavelet Elman neural network prediction model is established by combining the wavelet theory with the Elman neural network, and the initial weight and threshold of the network are improved by the mind evolutionary algorithm. It is applied to the prediction of the ultra-short term photovoltaic output. Using the wavelet function as a transfer function, which improves the function approximation ability and effectively solves the problem that the prediction model is easy to fall into the local minimum value, the iteration speed is slow and the prediction accuracy is not high in the prediction of light output. Finally, this paper is verified by the measured data of Dunhuang area. Compared with the traditional Elman neural network model, it shows that the model has a good effect on the ultra-short-term prediction of PV, and then provides decision-making assistance for dispatchers.