基于POI信息挖掘的网格空间负荷预测方法

Grid-based spatial load forecasting method based on POI information mining

空间负荷预测是配电网网格化规划的基础,互联网的发展使得将大数据技术应用于空间负荷预测成为了可能,本研究基于开源大数据,提出一种基于POI信息挖掘的网格空间负荷预测方法.首先基于python编程语言调用网络地图平台的API接口,获取开源的用户数据并利用射线法及正则匹配法进行信息挖掘,从而得到用于网格空间负荷预测的基础数据.其次参考配电网规划标准,建立规划区用地指标体系,并对基础数据进行归一化处理以反映各个网格的负荷分布情况.接着基于规划区具体情况,综合三种传统总量负荷预测方法进行总量负荷预测.最后结合总量负荷预测结果与负荷分布情况,基于总量负荷预测结果与网格负荷归一化占比分配总量负荷指标进行网格空间负荷预测,并拟合负荷发展特性曲线对预测结果进行验证.根据规划区实际情况应用本文中所述方法,可为配电网网格化规划提供依据.最后结合实际区域验证了所述方法的实用性.

Spatial load forecasting is the basis of grid planning for distribution networks.The development of the Internet makes it be possible to apply big data technology to spatial load forecasting.Based on open source big data,we proposed a grid-based spatial load forecasting method based on POI information mining.Firstly,the API interface of the network map platform was called based on Python programming language,the open source user data were obtained,and the ray method and regular matching method were used to carry out information mining,so as to obtain the basic data for grid-based spatial load forecasting.Secondly,by referring to the distribution network planning standards,the land use index system of the planned area was established,and the basic data were normalized to reflect the load distribution of each grid.Then,based on the specific situation of the planning area,three traditional total load forecasting methods were integrated for total load forecasting.Finally,combined with the total load prediction results and load distribution,grid-based spatial load forecasting was carried out based on the total load prediction results and grid load normalized proportion allocation total load index,and the forecasting results were verified by fitting the load development characteristic curve.Applying the method described in this article according to the actual situation of the planning area could provide a basis for grid planning of the distribution network.At the end of this article,the practicality of the method was verified in combination with the actual area.