Application of statistical and neural approaches to the daily load profiles modelling in power distribution systems
Application of statistical and neural approaches to the daily load profiles modelling in power distribution systems
复制标题
统计和神经方法在配电系统日常负载曲线建模中的应用
DOI：
--
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Z. Styczynski
中科院分区：
文献类型：
--
作者：
J. Nazarko;Z. Styczynski
负荷建模是配电系统经济分析、运行和规划中的一项重要任务。特别是，当在解除管制的能源市场上考虑到需求侧管理系统时，了解负荷概况是最重要的。基于负荷模型的日需求预测使用不同客户组合的可比较负荷研究数据。对于给定的季节和一周中的一天，日负荷曲线的形状主要取决于客户组成。在定义客观客户类别方面的困难使预测过程变得非常复杂。利用统计聚类和神经网络方法可以提高负荷建模的精度。本文提出了对配电系统长期规划有用的负荷建模方法。本文以波兰和德国的配电系统为例说明了这一理论。
Load modelling is an essential task in the economic analysis, operation and planning of distribution systems. Particularly, when a demand side management system is taken into account on a deregulated energy market, the knowledge of load profiles is of the greatest importance. Forecasting of daily demand, based upon load models, uses comparable load research data for a different customer mix. For the given season and day of the week, the shape of a daily load curve depends mainly on the customer composition. Difficulties in defining objective customer classes significantly complicate the forecasting process. Usage of statistical clustering and neural network approaches makes possible to improve the load modelling accuracy. This paper presents load modelling methods useful for the long-term planning of power distribution systems. The theoretical statement is illustrated by examples which correspond to Polish and German distribution systems.