摘要
随着新能源的大量接入和用户的广泛参与,电网企业的数据呈指数级增长,电网规划部门迫切需要运用大数据的分析手段提高规划决策的精准性。本文以地市供电公司的实体业务为切入点,基于地理位置信息,在数据融合贯通的基础上,运用数据可视化技术多维度动态展示地区电网负荷分布和电网供给能力,辅助规划人员快速发现负荷分布规律和电网薄弱点。运用大数据分析挖掘方法构建基于负荷特性分析的负荷预测模型,支撑电网项目和运行方式安排。打破电网负载问题的单一评价方法,建立可度量的电网综合评价体系及模型,为电网项目统筹提供量化依据。基于大数据的电网规划精益分析平台通过电网负荷可视化展示,有效提升了电网诊断分析效率,运用大数据分析方法提高了地区最大负荷的预测精度,实现了电网问题的数字化评估,提高了电网规划投资决策的精准性。
With the large number of new energy access and the wide participation of users,the data of power grid enterprises is growing exponentially. Power grid planning departments urgently need to use big data analysis methods to improve the accuracy of planning decisions. In this paper,the physical business of the municipal power supply company is taken as the entry point. Based on geographic location information and data fusion,this paper uses data visualization technology to dynamically display regional grid load distribution and grid supply capacity in multi-dimension,and assists planners to quickly discover the law of load distribution and grid vulnerability. A load forecasting model based on load characteristic analysis is constructed by using big data analysis and mining method to support grid project and operation mode arrangement. This paper breaks through the single evaluation method of power grid load problem,establishes a measurable power grid comprehensive evaluation system and model,and provides quantitative basis for power grid project co-ordination. The lean analysis platform of power grid planning based on big data can effectively improve the efficiency of power grid diagnosis and analysis through visualization of power grid load. The prediction accuracy of regional maximum load is improved by using big data analysis method,and the digital evaluation of power grid problems is realized,and the precision of investment decision in power grid planning is improved.
作者
谈韵
万顺
张谢
陈晨
TAN Yun;WAN Shun;ZHANG Xie;CHEN Chen(State Grid Anhui Electric Power Co.,Ltd.Hefei Power Supply Company,Hefei 230022 Anhui,China)
出处
《电力大数据》
2019年第2期34-40,共7页
Power Systems and Big Data
关键词
数据融合
数据可视化
数据分析挖掘
负荷预测
data fusion
data visualization
data analysis and mining
load forecasting