Next Article in Journal
Poisoning Effect of SO2 on Honeycomb Cordierite-Based Mn–Ce/Al2O3Catalysts for NO Reduction with NH3 at Low Temperature
Previous Article in Journal
Silicon Photonics towards Disaggregation of Resources in Data Centers
Open AccessArticle

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

1
Beijing Key Laboratory of Distribution Transformer Energy-Saving Technology, China Electric Power Research Institute, Beijing 100192, China
2
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(1), 93; https://doi.org/10.3390/app8010093
Received: 31 October 2017 / Revised: 5 January 2018 / Accepted: 7 January 2018 / Published: 11 January 2018
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. View Full-Text
Keywords: intelligent power distribution and utilization; heterogeneous data integration; generative adversarial networks; peak clustering; finite open coverage intelligent power distribution and utilization; heterogeneous data integration; generative adversarial networks; peak clustering; finite open coverage
Show Figures

Figure 1

MDPI and ACS Style

Tan, Y.; Liu, W.; Su, J.; Bai, X. Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization. Appl. Sci. 2018, 8, 93.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop