基于GRA-LSSVM的配电网空间负荷预测方法研究

Research on spatial load forecasting of distribution network based on GRA-LSSVM method

针对配电网空间负荷预测实际应用中容易存在可用信息和数据散杂且经常匮乏的问题,提出了一种基于最小二乘支持向量机的新型配电网空间负荷密度预测算法,以解决预测方法中样本有限、不易识别等问题。同时引入灰色关联分析改善最小二乘支持向量机的样本筛选,并采用混沌粒子群算法完善最小二乘支持向量机的参数选择,提高算法的空间负荷密度预测的精度。在介绍算法原理基础上,详细设计了配电网空间负荷预测方法的实现流程。对该算法的性能进行实例分析表明,所提方法可以有效地提高负荷密度预测的精度。

In the practical application of spatial load forecasting of distribution network,the available information and data are often scattered and poor.In this paper,a novel spatial load density prediction algorithm based on the Least Squares Support Vector Machine(LSSVM)is proposed to solve the problems of limited samples and difficulty in identification.In this algorithm,Grey Relational Analysis(GRA)is introduced to improve the sample selection of the Least Squares Support Vector Machine(LSSVM),and Chaos Particle Swarm Optimization(CPSO)algorithm is adopted to consummate the parameter selection of the least squares support vector machine,which improves the accuracy of spatial load density prediction of the algorithm.Based on the principle of algorithm,this paper designs a detailed implementation process for the spatial load prediction method of distribution network.The performance of this algorithm is analyzed with an example.The example calculation shows that the proposed method can effectively improve the precision of load density prediction.