基于ARIMA模型的V2G电能质量缺失数据预测OA
V2G power quality loss data prediction based on improved ARIMA
针对智能电网中V2G电能质量数据缺失的问题,提出了一种基于截断奇异值分解(SVD)和差分自回归移动平均模型(ARIMA)的电能质量缺失数据预测方法.该将各个时刻不同切片的电能质量数据表示为一个矩阵,并以矩阵为单位对缺失数据进行预测.实验结果表明,相对于其他经典的数据缺失预测方法,该方法在保证了较高的预测精度的同时,可以极大地减少缺失数据预测的时间开销.
To address the problem of missing power quality data in smart grids,this study proposes a prediction method based on truncated singular value decomposition(SVD)and the autoregressive integrated moving average(ARIMA)model.The proposed approach represents power quality data from different time slices as a matrix and performs missing data prediction at the matrix level.Experimental re-sults demonstrate that,compared to other classical missing data prediction methods,the proposed method not only achieves high prediction accuracy but also significantly reduces computational overhead.
孙华玲;马飞;闫超
曲阜师范大学图书馆,273165,曲阜市曲阜师范大学计算机学院,276826,山东省日照市曲阜师范大学计算机学院,276826,山东省日照市
信息技术与安全科学
智能电网电能质量缺失数据填补ARIMA
smart gridpower qualitymissing data imputationARIMA
《曲阜师范大学学报（自然科学版）》 2026 (2)
74-78,5
山东省重点研发计划(2025CXGC010113).
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