Wavelet-based neural network with genetic algorithm optimization for generation prediction of PV plants

In order to solve the problem of uncertainty of photovoltaic power generation forecast, the prediction accuracy of photovoltaic power station power generation is further improved. In this paper, a wavelet neural network (WNN) based genetic algorithm (GA) is proposed to optimize the output forecasting method of photovoltaic power plants. It adopts the wavelet basis function as the transfer function of the network, and regards the network connection weight, the scaling factor and the translation factor of the wavelet function as a genetic individual. Then, it obtains the optimal initial parameters of the network through individual optimization based on genetic algorithm, and then imports it into the network. The results show that: under four typical weather types, the experimental data measured by photovoltaic power plants every half an hour are imported into four types of back-propagation artificial neural network (BP-ANN), WNN, GA-BP and GA-WNN network for simulation and prediction. The GA-WNN network prediction model outperforms the other three prediction methods in considering prediction error, relative error, other error evaluation methods and prediction reliability. The GA-WNN network is more suitable for the power generation forecast of photovoltaic power plants.