Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

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  • Additional Information
    • Publication Information:
      Hindawi Limited, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Engineering (General). Civil engineering (General)
      LCC:Mathematics
    • Abstract:
      Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.
    • File Description:
      electronic resource
    • ISSN:
      1024-123X
      1563-5147
    • Relation:
      https://doaj.org/toc/1024-123X; https://doaj.org/toc/1563-5147
    • Accession Number:
      10.1155/2019/9843041
    • Rights:
      Journal Licence: CC BY
    • Accession Number:
      edsdoj.113a312041dd46b3925dcfc34b75f50d
  • Citations
    • ABNT:
      NING-KANG PAN; CHUNWAN LV. Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models. Mathematical Problems in Engineering, [s. l.], 2019. Disponível em: . Acesso em: 18 set. 2019.
    • AMA:
      Ning-Kang Pan, Chunwan Lv. Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models. Mathematical Problems in Engineering. 2019. doi:10.1155/2019/9843041.
    • APA:
      Ning-Kang Pan, & Chunwan Lv. (2019). Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models. Mathematical Problems in Engineering. https://doi.org/10.1155/2019/9843041
    • Chicago/Turabian: Author-Date:
      Ning-Kang Pan, and Chunwan Lv. 2019. “Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models.” Mathematical Problems in Engineering. doi:10.1155/2019/9843041.
    • Harvard:
      Ning-Kang Pan and Chunwan Lv (2019) ‘Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models’, Mathematical Problems in Engineering. doi: 10.1155/2019/9843041.
    • Harvard: Australian:
      Ning-Kang Pan & Chunwan Lv 2019, ‘Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models’, Mathematical Problems in Engineering, viewed 18 September 2019, .
    • MLA:
      Ning-Kang Pan, and Chunwan Lv. “Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models.” Mathematical Problems in Engineering, 2019. EBSCOhost, doi:10.1155/2019/9843041.
    • Chicago/Turabian: Humanities:
      Ning-Kang Pan, and Chunwan Lv. “Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models.” Mathematical Problems in Engineering, 2019. doi:10.1155/2019/9843041.
    • Vancouver/ICMJE:
      Ning-Kang Pan, Chunwan Lv. Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models. Mathematical Problems in Engineering [Internet]. 2019 [cited 2019 Sep 18]; Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.113a312041dd46b3925dcfc34b75f50d&custid=s8280428