TY - GEN
T1 - An Intelligent Management Mechanism for Residential Power under Software Defined Network
AU - Zeng, Wenru
AU - Du, Boxin
AU - Guo, Zhiwei
AU - Yu, Keping
AU - Gao, Xu
AU - Shen, Yu
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by Chongqing basic research and frontier exploration project of China under Grant cstc2018jcyjAX0638, State Language Commission Research Program of China under Grant YB135-121, the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044, and Scientific Program of Chongqing Technology and Business University under Grant ZDPTTD201917, Grant KFJJ2018071, and Grant 1952027.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - grey prediction
KW - intelligent management
KW - residential power
KW - residual correction
KW - software defined network (SDN)
UR - http://www.scopus.com/inward/record.url?scp=85102933636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102933636&partnerID=8YFLogxK
U2 - 10.1109/GCWkshps50303.2020.9367453
DO - 10.1109/GCWkshps50303.2020.9367453
M3 - Conference contribution
AN - SCOPUS:85102933636
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020
Y2 - 7 December 2020 through 11 December 2020
ER -