基于GAKNN方法的配电站时间序列缺失数据补全方法

Completion method for missing time series data of distribution station based on GAKNN method

配电站时间序列数据从采集、传输到存储的过程中可能出现数据记录缺失的情况,在一定程度上影响高层级的数据分析及处理。针对这一问题,提出一种基于灰色自适应K-最近邻(GAKNN)方法的缺失数据补全方法。首先构建时间序列特征,然后在朴素K-最近邻(KNN)方法的基础上设置阈值筛选最近邻点,并结合灰色关联系数计算近邻点权重系数,最终依次补全缺失数据。以江苏省某市的电力数据样本进行实验,结果表明与其他方法进行对比,基于GAKNN方法的缺失数据补全方法的结果更好,并且补全后的样本在深度学习预测中具有更低的误差。

Data record loss may occur in the process of time series data acquisition,transmission and storage in distribution station,which affects high-level data analysis and processing to a certain extent.To solve this problem,a completion method for missing data based on GAKNN(Gray Adaptive K-Nearest Neighbor)method is proposed.Firstly,the time series features are constructed.Then,the nearest neighbor points are selected by the threshold based on simple KNN(K-Nearest Neighbor)method,and the weight coefficients of the nearest neighbor points are calculated combining with the gray correlation coefficient.Finally,the missing data can be completed in turn.With the electric power data sample of a city in Jiangsu Province,the test results show that the missing data completion results of GAKNN method are better than those of other methods,and the completed samples have lower errors in deep learning prediction.