Abstract: For the complex sequence with the characteristics of annual fluctuation and sea‐ sonal fluctuation，a grey prediction model based on seasonal factors，particle swarm optimi‐ zation（PSO），and Fourier optimization was used in this paper to achieve accurate prediction of seasonal fluctuation series. Firstly，this prediction model proposed three seasonal factors by changing the annual effect coefficient and then compared these factors. Secondly，to re‐ duce the interference of time variation on the sequence，this paper added linear correction terms to the prediction model and used the PSO algorithm to find the optimal parameters to improve the prediction accuracy of the model. Finally，by considering the influence of season‐ al variation on the sequence，the Fourier series was used to fit the residual series of the mod‐ el. In this paper，the model was applied to the simulation and prediction of the net hydroelec‐ tric power generation in China，and the final error was 1. 22%. The research shows that the model has higher prediction accuracy for fluctuation sequences.

Key words: seasonal factor, periodic sequence, grey model, particle swarm optimization, Fourier series