Combined forecasting of terminal load based on grey depth belief network

Li Zhang, Zhiyun Sun, Jingjing Huang, Xiaolong Lu, Hewei Chen and Qizhen Wei

International Journal of Global Energy Issues, 2024, vol. 46, issue 6, 567-584

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.

Keywords: grey model; deep belief network; urban transportation complex; load combination forecasting; passenger throughput; historical data of energy consumption; renewable energy. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijgeni:v:46:y:2024:i:6:p:567-584

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