基于灰色模糊预测的智能配电网大数据调度算法

Novel SPDU Big Data Scheduling Algorithm Based on Grey Fuzzy Prediction

针对智能配电网大数据服务系统存在数据量大、信息复用困难、调用跨度大等问题,提出了一种基于灰色模糊预测的配电网大数据调度算法(SGFP)。基于灰色模糊预测方法对配电网管理人员和客户潜在需求进行预测,对多服务器中流动数据实施预存取与调度,从而避免和减少了智能配电网大数据服务系统中出现的存取瓶颈和大跨度调用现象。SGFP算法的性能在面向社区用户的用电互动服务系统中有所体现,相较于目前常用算法其预取数据的响应速度与准确度具有较大优势,系统资源开销较低,具有较高的性价比。

For the problems existing in smart power distribution and utilization(SPDU)data service system,such as big data size,difficult information retrieval and long span scheduling,a novel SPDU big data scheduling algorithm based on grey fuzzy prediction(SGFP)is presented.In order to address the data access bottlenecks and long span scheduling of SPDU big data systems,the SGFP utilizes the grey fuzzy methods to mine the requirements of clients and management personals,and to make pre-access and scheduling of the moving data in several data servers.The SGFP is tested in an interactive community client service system.The simulation result shows that compared to the traditional algorithms,the SGFP has better response speed and accuracy,as well as lower system resource cost.