Design of Artificial Intelligence Power System Prediction Model by Fusing GSO Algorithm and AFSA Algorithm
In order to solve the poor stability and low utilization of traditional statistical models in power sys-tem load forecasting,the study introduces artificial intelligence into the statistical model of power system.The study innovatively optimizes and integrates the artificial firefly algorithm and the artificial fish swarm algorithm,and uses them to construct the power system load forecasting model.Firstly,the artificial firefly algorithm is opti-mized,and then the optimized artificial firefly algorithm is fused with the artificial fish swarm algorithm for con-structing the power load prediction model,and finally the performance of the prediction model is verified by u-sing simulation experiments.The results show that the fluctuation range of the node voltage is obviously smoother through the normalization of the prediction model,and its fluctuation range is distributed in[0.961-1.00].Meanwhile the prediction model obtained the optimal solution in iterations up to 66 times,which is also signifi-cantly better than the comparison algorithm.This indicates that the artificial intelligence power system load pre-diction model incorporating the artificial firefly algorithm and the artificial fish swarm algorithm shows superiority in terms of accuracy and stability.