An Intelligent Management Mechanism for Residential Power under Software Defined Network

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728173078
DOI
出版ステータスPublished - 2020 12
イベント2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
継続期間: 2020 12 72020 12 11

出版物シリーズ

名前2020 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

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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