An Intelligent Management Mechanism for Residential Power under Software Defined Network

Wenru Zeng, Boxin Du, Zhiwei Guo, Keping Yu, Xu Gao, Yu Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The residential power is the lifeblood of the national economy, and its efficient management is of great significance. Precise prediction of the residential power demand has always been a most important concern of management mechanism which can be carried by a software defined network (SDN) platform. However, existing methods are heavily reliable on data with multiple features and high dimensions, failing to discovering sequential characteristics from simple and sparse data. In this paper, we develop a residual correction-based grey prediction model for residential power management under SDN. In detail, the residual function is used to correct the prediction value of the traditional gray model, so that prediction accuracy can be improved. Besides, a set of computational experiments are carried out on real-world business data to assess precision accuracy of the proposed model. It is concluded through experiments that the proposed model can better predict residential power demand.

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
Publication statusPublished - 2020 Dec
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
CountryTaiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

Keywords

  • grey prediction
  • intelligent management
  • residential power
  • residual correction
  • software defined network (SDN)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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