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

机译：自回归综合移动平均线和背部经历神经网络模型预测地区的主要能量要求

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摘要

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.
机译：预测能源数据，尤其是主要能源要求，是政策制定的关键部分。对于不同发展类型的领土，寻求基于知识和可靠的预测模型是政策制定繁荣发展的重要前提。在本文中，已被证明在预测中被证明非常有效的自回归综合移动平均线和深度效率的神经网络模型适用于三种不同发展类型的地区的主要能耗预测。结果表明，实际数据和模拟值之间的平均相对误差由自回归综合移动平均值的4.5％至5.9％，并通过反向化神经网络的0.04％至0.47％。特别是，该研究表明，在考虑国内生产总值，人口和特定价值作为预测因子时，反向化神经网络模型提高了原发性能源的更好预测。此外，我们表明单输入背部化神经网络模型仍然可以在特定值对能量消耗贡献的影响时工作。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第17期|9843041.1-9843041.14|共14页
  • 作者

    Pan Ning-Kang; Lv Chunwan;

  • 作者单位

    Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China;

    Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China;

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