摘要
电力系统的负荷是不确定、非线性、动态开放性的复杂大系统,传统方法往往难以准确地描述这种系统的复杂非线性特征,因而无法进行更精确负荷预测。该文提出了一种基于时间序列的进化支持向量机(SVM)的负荷预测方法。该方法避免了SVM方法人为控制核函数和参数的传统模式,而是采用单纯形—小生境遗传算法对其进行快速的局部和全局寻优,具有更好的泛化性能和收敛精度,减少了对经验的依赖。同时,时间序列考虑了趋势分量和周期分量,使负荷预测模型更加符合电力负荷特性。该方法在电网实际负荷预测中和真实值的比较证明本文提出的负荷预测模型是最优的实用模型。
Because power load system was an uncertain, nonlinear, dynamic and complicated system, it was difficult to describe such a nonlinear characteristics of this system by traditional methods, so the load forecasting could not be accurately forecasted. An evolutionary Support Vector Machines (SVM) algorithm based on time sequence was brought forward in the paper. The algorithm avoided traditional model of SVM to control the kernel function and parameters and found the local and global optimization with Simplex-Niche-Genetic algorithm, which was more generalized and its dependence on experience was weakened. In the time sequence the trend component and periodical component were considered to make the load forecasting model more coincident with the features of power loads. Applying the presented method to actual load forecasting, the comparison among the forecasted results and the true shows that the presented method is inferior to none, feasible and effective.
出处
《电网技术》
EI
CSCD
北大核心
2006年第S2期595-599,共5页
Power System Technology
关键词
负荷预测
支持向量机
时间序列
单纯形-小生境遗传算法
Power load forecasting s Support Vector Machines
Time series
Simplex-Niche-Genetic algorithm
