基于低秩张量补全的非侵入式负荷监测缺失数据修复方法

Non-intrusive Load Monitoring Missing Data Recovery Based on Low-rank Tensor Completion

非侵入式负荷监测技术(non-intrusive load monitoring,NILM)作为实现智能电网用户侧细粒度感知的重要手段,有助于实现需求响应、提高“源-网-荷”互动效率和优化用能,助力实现“30·60目标”。高质量的量测信息是数据驱动型NILM的基础,但由于数据采集装置故障、通道拥塞以及延时等都会导致数据缺失,尤其是严重的连续性缺失,由此造成非侵入式负荷监测与分解的精度下降,影响用户画像、需求响应等高级应用。因此,针对该问题,提出了一种基于CP分解的正则化低秩张量补全的量测数据缺失修复方法。算法突破传统单维数据处理局限,对NILM多维量测数据构建了三阶观测张量,从而利用数据内部时序关联性和参量维度间电气关联性进行正则化低秩张量补全。并针对每次核范数计算过程中奇异值分解计算量过大问题,采用基于CP因子矩阵分解的核范数计算降低计算量,减少计算时长,并证明了变换的等效性。最后基于NILM公开数据集iAWE进行了实验,实验结果表明所提出的方法可以提高数据修复精度,在高缺失率和连续缺失情况下仍能有较好地补全效果,并且通过非侵入式负荷分解实验证明其可有效提高分解精度,对智能电网提升细粒度感知能力具有良好的实际意义。

Non-intrusive load monitoring(NILM),as an important means to achieve fine-grained perception of the smart grid users,helps to achieve demand response,high efficiency‘source-grid-load’interaction and optimal energy use,strongly promoting the realization of the"3060 goal".The high-quality measurement is the foundation of the data-driven NILM.However,the data acquisition device failures,the channel congestion and delay may lead to data loss,especially the seriously continuous loss,resulting in a decrease in the accuracy of the non-intrusive load monitoring and disaggregation and affecting some advanced applications such as the user profiling and the demand response.To tackle this problem,a new data recovery approach for the missing NILM measurements based on the regularized low-rank tensor completion is proposed.Breaking through the limitations of the traditional single-dimensional data processing,this approach constructs a third-order observation tensor across the NILM multi-dimensional measurements,and formulates a regularized low-rank tensor completion so as to exploit the internal time series correlation of the measurements and the electrical correlation between the electrical parameters.Considering the calculation of the singular value decomposition of large factor matrices at each iteration usually takes too much time,the calculation of the trace norm based on the Canonical Polyadic factor matrix decomposition is used to reduce the calculation amount and save the calculation time,and mathematically proves the equivalence of the transformation.Finally,the promising experimental results on the NILM public dataset iAWE show the proposed method is able to improve the accuracy of the data recovery,and have a good completion effect under the conditions of having high missing rates or continuous losses.What’s more,through the non-intrusive load disaggregation experiments,the proposed method is able to effectively improve the disaggregation accuracy,having a good practical significance for improving the fine-grained perception ability of a smart grid.