考虑地域差异的配电网空间负荷聚类及一体化预测方法

发布时间：2018-04-11 07:00

本文选题：空间负荷预测 + 负荷密度指标法　；参考：《电力系统自动化》2017年03期

【摘要】：针对基于智能算法的负荷密度指标法对样本依赖性强且在各地实际应用困难的不足,提出一种考虑地域差异的配电网空间负荷聚类及一体化预测方法。该方法首先通过大量调研得到分布在不同地区、分属不同类型的负荷样本及所处地区信息;然后利用基于日负荷曲线的负荷分类校验及精选方法对所有调研样本进行分类精选;再根据区域分类、负荷分类对精选样本构成的全样本空间进行两级划分,得到分层级子样本空间;最后根据待测地块的属性信息对子样本空间进行匹配,选取与其最相似的子样本空间作为训练样本,构建支持向量机模型预测各地块的负荷密度,进而得到电力负荷的空间分布。工程实例分析表明了该方法的实用性和有效性。
[Abstract]:In view of the shortage of intelligent algorithm based load density index method which is highly dependent on samples and difficult to be applied in various places, a method of spatial load clustering and integrated forecasting considering regional differences is proposed.Firstly, the load samples distributed in different regions and different types of load samples are obtained by a large number of investigations, and then the load classification checking and selecting method based on daily load curve is used to classify and select all the investigation samples.Then according to regional classification and load classification, the whole sample space of selected samples is divided into two levels, and the sub-sample space is obtained. Finally, the sub-sample space is matched according to the attribute information of the plots to be measured.The most similar subsample space is chosen as the training sample, and the support vector machine model is constructed to predict the load density of each plot, and then the spatial distribution of power load is obtained.An engineering example shows the practicability and effectiveness of the method.
【作者单位】：浙江大学电气工程学院;国网浙江省电力公司经济技术研究院;
【基金】：国家电网公司科技项目(5211JY150016)~~
【分类号】：TM715

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