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Neural Network Model with Monte Caro Algorithm for Electricity Demand Forecasting in Queensland

Conference Paper


Abstract


  • With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland’s electricity demand forecasting.

Authors


  •   Yong, Binbin (external author)
  •   Xu, Zijian (external author)
  •   Shen, Jun
  •   Chen, Huaming (external author)
  •   Tian, Yanshan (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2017

Citation


  • Yong, B., Xu, Z., Shen, J., Chen, H., Tian, Y. & Zhou, Q. (2017). Neural Network Model with Monte Caro Algorithm for Electricity Demand Forecasting in Queensland. Australasian Symposium on Grid Computing and e-Research (now AusPDC) (pp. 47:1-47:7). New York: ACM Digital Library.

Scopus Eid


  • 2-s2.0-85014937145

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6459

Start Page


  • 47:1

End Page


  • 47:7

Place Of Publication


  • New York

Abstract


  • With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland’s electricity demand forecasting.

Authors


  •   Yong, Binbin (external author)
  •   Xu, Zijian (external author)
  •   Shen, Jun
  •   Chen, Huaming (external author)
  •   Tian, Yanshan (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2017

Citation


  • Yong, B., Xu, Z., Shen, J., Chen, H., Tian, Y. & Zhou, Q. (2017). Neural Network Model with Monte Caro Algorithm for Electricity Demand Forecasting in Queensland. Australasian Symposium on Grid Computing and e-Research (now AusPDC) (pp. 47:1-47:7). New York: ACM Digital Library.

Scopus Eid


  • 2-s2.0-85014937145

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6459

Start Page


  • 47:1

End Page


  • 47:7

Place Of Publication


  • New York