Tensor Decomposition Based Electrical Data Recovery
As the development of smart grid and energy internet, the amount of transmitted data in real time significantly increase. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation and periodicity of electricity data which is generated by human’s actions and feelings, we treat the electricity data as a tensor where the three dimensional are user, weeks, days. We divide the electricity data tensor into the sum of multiple rank-1 tensors and use the known data to approximate the electricity data tensor and recover the lost electrical data. Based on the real electricity data, we analyze the sparseness of the electricity data tensor and perform the CP decomposition-based method on the real data. The experimental results verify the recovery efficiency of the proposed scheme.
KeywordsElectrical data recovery Tensor factorization Sparse
This work was supported by National Natural Science Foundation of China (Nos. 61802030, 61572184, 61502054), the Science and Technology Projects of Hunan Province (No. 2016JC2075), the Research Foundation of Education Bureau of Hunan Province, China (Nos. 16C0047, 16B085).
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