国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to improve the accuracy of short-term load forecasting, this paper presents an efficient algorithm for parameter optimization of Support Vector Machine. The algorithm first sorts the initial particle swarm fitness, and then divides the initial particle swarm into two groups according to the size of the fitness, and simultaneously uses the different weights for global search and local search. The number of global search particles is much larger than that of local search, and the larger global inertia weight is used. The local search particle group uses a smaller inertia weight. With the increase of the number of iterations, the number of global search particles is decreasing, the number of local search is increasing, the number of particles of two groups is dynamically changing. And the average particle size and fitness variance are introduced to solve the problem that the particle group is easy to fall into the local optimum. Finally, the improved dynamic two group particle swarm optimization algorithm is used to optimize the parameters of the least squares support vector machines for short-term load forecasting. The experimental results show that the proposed method has higher prediction accuracy and is feasible and effective.