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Combined forecasting of terminal load based on grey depth belief network

Author

Listed:
  • Li Zhang
  • Zhiyun Sun
  • Jingjing Huang
  • Xiaolong Lu
  • Hewei Chen
  • Qizhen Wei

Abstract

In order to improve the prediction accuracy of electric energy consumption of civil aviation airport terminal, a combined prediction model of terminal load based on grey depth belief network is proposed. Firstly, the operation data of the airport is analysed to determine the main factors affecting the power consumption of the airport terminal. Then, the improved grey prediction model is established by using the historical data of electric energy consumption, and the grey prediction results, the characteristics of multidimensional historical power consumption data and the main factors affecting electric energy consumption are taken as the inputs of the deep belief network. Finally, the power consumption of the terminal is predicted based on this model. The experimental results show that the proposed grey depth belief network combination model has low prediction error, and the Mean Square Error (MSE) and Mean Relative Error (MRE) of the proposed model are 0.0988 and 0.0033.

Suggested Citation

  • Li Zhang & Zhiyun Sun & Jingjing Huang & Xiaolong Lu & Hewei Chen & Qizhen Wei, 2024. "Combined forecasting of terminal load based on grey depth belief network," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 46(6), pages 567-584.
    • Handle: RePEc:ids:ijgeni:v:46:y:2024:i:6:p:567-584
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