Title: Combined forecasting of terminal load based on grey depth belief network

Authors: Li Zhang; Zhiyun Sun; Jingjing Huang; Xiaolong Lu; Hewei Chen; Qizhen Wei

Addresses: Carbon Neutrality Development Research Institute of CPECC, China Power Engineering Consulting Group Co., Ltd., Xicheng District, Beijing, China ' New Forms of Energy Technology Centre, China Energy Engineering Group Hunan Electric Power Design Institute Co., Ltd., Changsha, Beijing, China ' Carbon Neutrality Development Research Institute of CPECC, China Power Engineering Consulting Group Co., Ltd., Xicheng District, Beijing, China ' New Forms of Energy Technology Centre, China Energy Engineering Group Hunan Electric Power Design Institute Co, Ltd., Beijing, Changsha, China ' New Forms of Energy Technology Centre, China Energy Engineering Group Hunan Electric Power Design Institute Co, Ltd., Beijing, Changsha, China ' New Forms of Energy Technology Centre, China Energy Engineering Group Hunan Electric Power Design Institute Co, Ltd., Beijing, Changsha, China

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.

DOI: 10.1504/IJGEI.2024.141922

International Journal of Global Energy Issues, 2024 Vol.46 No.6, pp.567 - 584

Received: 14 Nov 2022
Accepted: 25 Jan 2023
Published online: 03 Oct 2024 *

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