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Combined General Vector Machine for Single Point Electricity Load Forecast

  • Binbin Yong
  • Yongqiang Wei
  • Jun Shen
  • Fucun Li
  • Xuetao Jiang
  • Qingguo ZhouEmail author
Conference paper
  • 7 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 551)

Abstract

General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

Keywords

General Vector Machine Electricity load forecast Time series forecast Combined model 

Notes

Acknowledgments

This work was partially supported by National Natural Science Foundation of China under Grant No. 61402210, The Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2018-k12, Ministry of Education - China Mobile Research Foundation under Grant No. MCM20170206, Major National Project of High Resolution Earth Observation System under Grant No. 30-Y20A34-9010-15/17, State Grid Corporation Science and Technology Project under Grant No. SGGSKY00FJJS1700302, No. 52272218002K and No. SGGSKY00FJJS1800403, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, and Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Binbin Yong
    • 1
    • 2
  • Yongqiang Wei
    • 1
  • Jun Shen
    • 3
    • 4
  • Fucun Li
    • 3
  • Xuetao Jiang
    • 1
  • Qingguo Zhou
    • 1
    Email author
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Physical Science and TechnologyLanzhou UniversityLanzhouChina
  3. 3.School of Information Systems and TechnologyUniversity of WollongongWollongongAustralia
  4. 4.Department of EE and CS, Research Lab of ElectronicsMassachusetts Institute of TechnologyCambridgeUSA

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