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Electricity Consumption Prediction for Xinjiang Electric Energy Replacement

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Listed:
  • Xinfu Song
  • Gang Liang
  • Changzu Li
  • Weiwei Chen

Abstract

In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.

Suggested Citation

  • Xinfu Song & Gang Liang & Changzu Li & Weiwei Chen, 2019. "Electricity Consumption Prediction for Xinjiang Electric Energy Replacement," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, March.
  • Handle: RePEc:hin:jnlmpe:3262591
    DOI: 10.1155/2019/3262591
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    Cited by:

    1. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    2. Szilassy, Péter Ákos & Földes, Dávid, 2022. "Consumption estimation method for battery-electric buses using general line characteristics and temperature," Energy, Elsevier, vol. 261(PA).

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