Variable score self-encoder based method for filling in missing big data on electricity consumption information

Yong Shi; ZhengXiong Mao; Fu Bao; Yuan Tian; Hang Zhang

doi:10.1117/12.2674864

2 May 2023 Variable score self-encoder based method for filling in missing big data on electricity consumption information

Yong Shi, ZhengXiong Mao, Fu Bao, Yuan Tian, Hang Zhang

Author Affiliations +

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420V (2023) https://doi.org/10.1117/12.2674864
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In the context of the problem of excessive MAPE values in the method of filling in missing big data of electricity consumption information, a method of filling in missing big data of electricity consumption information based on variational self-encoder is designed. Optimising the electricity information pre-processing model, store and manage the various types of raw and application data collected in a classified manner, define the objective function as the algebraic sum of the squared measurement errors, construct an electricity big data tensor filling model, treat missing values as variables, and design a missing filling method based on a variational self-encoder. Experimental results: The mean value of MAPE of the big data missing fill method for electricity consumption information in the paper is: 38.514%, indicating that the performance of the designed big data missing fill method for electricity consumption information is better after fusing the variational self-encoder.

Citation Download Citation

Yong Shi, ZhengXiong Mao, Fu Bao, Yuan Tian, and Hang Zhang "Variable score self-encoder based method for filling in missing big data on electricity consumption information", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420V (2 May 2023); https://doi.org/10.1117/12.2674864

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