考虑数据时空分布特征的建筑群电力负荷填补方法OA

To assist power companies and load aggregators in addressing the issue of missing data in building clusters,a filling method,which utilized the spatiotemporal distribution characteristics of the data,was proposed for imputing missing power load data.Based on the spatiotemporal features of the data,the building clusters load data were divided for learning and reconstruction.Based on K-SVD(K-singular value decomposition),a load dictionary was constructed by learning from complete samples to capture power consumption patterns.The observed portions of incomplete samples were utilized to identify consumption patterns,which can facilitate the reconstruction of the missing data.The effectiveness of the proposed method was validated by real-world power load data collected from a specific region,and its imputation performance was compared and analysed in both spatial and temporal dimensions.The results demonstrated that the proposed K-SVD-based imputation method achieved low imputation errors and maintained low computational costs.When the missing rate was 70%,the MAPE and CVRMSE of the temporal K-SVD reached 3.7%and 4.5%,respectively.The spatial K-SVD performed better than the temporal K-SVD,with maximum reductions in MAPE and CVRMSE of up to 9%and 10.1%,respectively.