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A novel Monte Carlo-based neural network model for electricity load forecasting

Journal Article


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Abstract


  • The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the electricity load dataset of Queensland, Australia. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we specially propose to use the weights-fixed method, ReLu activation function, an efficient algorithm for reducing time and the influence of parameter matrix β to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.

UOW Authors


  •   Yong, Binbin (external author)
  •   Xu, Zijian (external author)
  •   Shen, Jun
  •   Chen, Huaming (external author)
  •   Wu, Jianqing (external author)
  •   Li, Fucun (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Xu, Z., Shen, J., Chen, H., Wu, J., Li, F. & Zhou, Q. (2020). A novel Monte Carlo-based neural network model for electricity load forecasting. International Journal of Embedded Systems, 12 (4), 522-533.

Scopus Eid


  • 2-s2.0-85086078440

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5088&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4061

Number Of Pages


  • 11

Start Page


  • 522

End Page


  • 533

Volume


  • 12

Issue


  • 4

Place Of Publication


  • United Kingdom

Abstract


  • The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the electricity load dataset of Queensland, Australia. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we specially propose to use the weights-fixed method, ReLu activation function, an efficient algorithm for reducing time and the influence of parameter matrix β to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.

UOW Authors


  •   Yong, Binbin (external author)
  •   Xu, Zijian (external author)
  •   Shen, Jun
  •   Chen, Huaming (external author)
  •   Wu, Jianqing (external author)
  •   Li, Fucun (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2020

Citation


  • Yong, B., Xu, Z., Shen, J., Chen, H., Wu, J., Li, F. & Zhou, Q. (2020). A novel Monte Carlo-based neural network model for electricity load forecasting. International Journal of Embedded Systems, 12 (4), 522-533.

Scopus Eid


  • 2-s2.0-85086078440

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5088&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4061

Number Of Pages


  • 11

Start Page


  • 522

End Page


  • 533

Volume


  • 12

Issue


  • 4

Place Of Publication


  • United Kingdom