Daily solar radiation prediction based on Genetic Algorithm Optimization of wavelet neural network

Daily solar radiation prediction is a nonlinear and non-stationary process. It´s hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transform. Input weights, output weights, scale factors and translation factors were optimized by genetic algorithm. Gradient descent method was used to make further training of the model with temperature, clearness index, and daily radiation data. Simulation results indicate that the method is satisfactory to the prediction of daily solar radiation.