基于极限学习机与负荷密度指标法的空间负荷预测

Spatial load forecasting based on ELM and clustering algorithm

空间负荷预测对有配电网的规划建设具有重要意义,为了提高配电网空间负荷预测的精度,文中提出基于极限学习机(ELM)的配电网空间负荷预测算法,采用粒子群优化(PSO)模型的参数。首先根据用地性质将负荷分类,再通过模糊C均值(FCM)算法对每一类负荷进行聚类分析,建立精细化的负荷密度指标体系。根据待预测地块的特性指标选取训练样本,代入ELM训练,提高预测精度。通过搜索的数据对实例进行仿真试验,通过对比未引入FCM算法的相对误差、未引入PSO算法的相对误差以及采用PSO-ELM算法的相对误差可得,文中提出的PSO-ELM算法具有较高精度,满足实际工程的要求。

Spatial load forecasting is of great significance to the planning and construction of distribution network.In order to improve the accuracy of spatial load forecasting of distribution network,based on extreme learning machine,a spatial load forecasting algorithm is put forward in this paper.The parameters of the particle swarm optimization mode are adopted.Firstly,the load is classified according to the property of land use.Then,the FCM algorithm is used to carry out cluster analysis for each type of load and a refined load density index system is established.Next,the training samples are carried out with the extreme learning machine to improve the accuracy of prediction,which selected according to the characteristic indexes of the plots to be predicted.The example is simulated by the search data.By comparing with the relative error without the introduction of FCM algorithm,the relative error without the introduction of PSO optimization algorithm and the relative error with the adoption of PSO-ELM algorithm,it can be obtained that the PSO-ELM algorithm proposed in this paper has a high accuracy and meets the requirements of practical engineering.