Neural network wind speed prediction based on multiple prediction model and nonlinear combination - Jiajun WANG; Wei CAO; Guilong ZHANG; Huaizhi ZHANG; Zixing LING; Xiaoqiang ZHAO (物联网学报)
For the problem of strong randomness in space-time characteristics of wind speed in complex mountains，in order to improve the accuracy of wind speed data prediction, a neural network wind speed prediction algorithm based on multi prediction model and nonlinear combination was proposed.In the first layer of the algorithm, the grey wolf optimizer (GWO) and the dynamic convergence factor were used to improve the whale optimization algorithm (WOA), and the improved WOA was applied to the updating process of BPNN weights and bias items.At the same time, the improved whale optimiza-tion algorithm of back propagation neural network (IWOABP), ELM and LSTM three complementary single methods were constructed to build a combination prediction method, and on this basis, the ELM mixing mechanism of the second layer of the algorithm was utilized to learn the relationship between the first layer and the final result in a non-linear way.Simulation results show that compared with BPNN, WNN and GWOBP, the proposed algorithm has lower prediction errors.