Power project cost prediction based on random forest optimization RL
Aiming at the problem of lower accuracy of power project cost data prediction at the present stage,a power project cost prediction algorithm based on random forest optimization RL was proposed.By analyzing the factors that affect the cost of power projects,the random forest algorithm was used to screen the factors according to the degree of impact on the cost of power projects.The filtered factor eigenvector was used as the training and testing data of the power project cost prediction model.The problem of power project cost prediction was transformed to the problem of transmission line planning,and the parameters of ant colony algorithm were optimized by reinforcement learning to build the power project cost prediction model.Through the contrast experiment,in combination with the results for two grid sizes,the power project cost described in this paper is reduced by 2.97％and 3.78％,respectively,compared with the two contrast group algorithms.
power transmission and transformation projectproject costrandom forestreinforcement learningant colony algorithmtransmission line planningdata predictioneigenvector
万方数据
维普
