电力大数据的价值密度评价及结合改进k-means的提升方法研究

Evaluation and Promotion Methods with Improved k-means for Value Density of Electric Power Big Data

针对目前电力大数据价值密度的研究存在缺乏定义和量化指标、提升手段单一导致效果有限的问题,提出了相关定义及评价指标,从空间上内存占用、时间上运行速率2个维度计算价值密度评价指标;并提出了基于多初始聚类中心的改进k-means算法,弥补其太过依赖于初始聚类中心的不足。结合该算法,分别从"脏数据"、记录、字段等不同维度,研究如何提升价值密度。以日负荷预测为算例进行仿真测试,结果表明评价指标能较好地反映价值密度,改进聚类算法有较好的的聚类效果和速率优势,可以有效提升数据价值密度。

Study on the value density of electric power big data lacks of quantitative evaluation index and promotion methods are limited,resulting in limited effect.In the view of this problem,the paper proposes the evaluation index of value density based on memory footprint and operation speed,and puts forward the multiple initial clustering centers-based improved k-means algorithm to make up for the problem that it relies too much on the initial cluster center.Combined with the algorithm,this paper improves the value density from different dimensions such as "dirty data",records and fields.Taking daily load forecasting as the simulation test,the results show that the defined index can well reflect the value density.The improved algorithm has better clustering effect and speed advantage,and can effectively enhance the data value density.