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Combined General Vector Machine for Single Point Electricity Load Forecast

Conference Paper


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Abstract


  • General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GYM 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 GYM, BPNN, SVM and ARIMA are proposed and verified. Results show that GYM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

Authors


  •   Yong, Binbin (external author)
  •   Wei, Yongqiang (external author)
  •   Shen, Jun
  •   Li, Fucun (external author)
  •   Jiang, Xuetao (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Wei, Y., Shen, J., Li, F., Jiang, X. & Zhou, Q. (2020). Combined General Vector Machine for Single Point Electricity Load Forecast. The 9th International Conference on Frontier Computing (FC2019) Theory, Technologies and Applications (pp. 284-290). LNEE: Springer.

Scopus Eid


  • 2-s2.0-85082339181

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4788&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3762

Start Page


  • 284

End Page


  • 290

Place Of Publication


  • LNEE

Abstract


  • General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GYM 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 GYM, BPNN, SVM and ARIMA are proposed and verified. Results show that GYM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

Authors


  •   Yong, Binbin (external author)
  •   Wei, Yongqiang (external author)
  •   Shen, Jun
  •   Li, Fucun (external author)
  •   Jiang, Xuetao (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Wei, Y., Shen, J., Li, F., Jiang, X. & Zhou, Q. (2020). Combined General Vector Machine for Single Point Electricity Load Forecast. The 9th International Conference on Frontier Computing (FC2019) Theory, Technologies and Applications (pp. 284-290). LNEE: Springer.

Scopus Eid


  • 2-s2.0-85082339181

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=4788&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3762

Start Page


  • 284

End Page


  • 290

Place Of Publication


  • LNEE