Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

Abstract

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.Abstract: Heterogeneous characteristics of a big data system for intelligent power distribution and utilization have already become more and more prominent, which brings new challenges for the traditional data analysis technologies and restricts the comprehensive management of distribution network assets. In order to solve the problem that heterogeneous data resources of power distribution systems are difficult to be effectively utilized, a novel generative adversarial networks (GANs) based heterogeneous data integration method for intelligent power distribution and utilization is proposed. In the proposed method, GANs theory is introduced to expand the distribution of completed data samples. Then, a so-called peak clustering algorithm is proposed to realize the finite open coverage of the expanded sample space, and repair those incomplete samples to eliminate the heterogeneous characteristics. Finally, in order to realize the integration of the heterogeneous data for intelligent power distribution and utilization, the well-trained discriminator model of GANs is employed to check the restored data samples. The simulation experiments verified the validity and stability of the proposed heterogeneous data integration method, which provides a novel perspective for the further data quality management of power distribution systems.

Generative Adversarial Network and Its Application in Energy Internet

“…erefore, solving the problem of small sample learning is the key to the in-depth application of deep learning in smart grid. Tan et al [67] proposed an integration technology of intelligent distribution and utilization of heterogeneous data based on GAN for solving the problem that intelligent allocation and utilization in small sample environment with low utilization of heterogeneous data resources. By introducing GAN theory, the method expands the sample space according to the target samples whose measurement indexes have been completed.…”

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

“…In addition, the China Electric Power Research Institute and Tsinghua University have jointly investigated the heterogeneous data integration using GANs and applied it to the intelligent power distribution and utilization systems. In this research, a novel GAN‐based heterogeneous data integration method was proposed, which consists of two parts: a GAN model and a peak clustering model.…”

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