Fusion model with attention mechanism for carbon-neutral sports competitions

Frontiers in Ecology and Evolution

Ecology and Evolution

Xuan Zhang¹  Jun Zhang² 

[1] Department of Computer Science, University of Pennsylvania, Philadelphia, PA, United States;WuShu College, Shandong Sport University, Rizhao, China;

关键词: CNN;    GRU;    attention mechanism;    carbon neutral;    sports competitions game;    energy saving and emission reduction;   

DOI  :  10.3389/fevo.2023.1212732

 received in 2023-04-26, accepted in 2023-06-01,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionIn sports competitions, using energy-saving and emission-reduction measures is an important means to achieve the carbon neutrality goal.MethodsIn this paper, we propose an attention mechanism-based convolutional neural network (CNN) combined with the gated recurrent unit (GRU) for carbon neutral energy saving and emission reduction prediction model in sports competitions, CNN is a feedforward neural network whose input is a two-dimensional matrix. The main feature of CNN is that it can handle multi-channel input data, and the use of GRU can make the model structure simple and largely reduce The use of GRU can make the model structure simple, which largely reduces the hardware computational power and time cost and also better solves the long dependency problem in RNN networks. CNN-GRU extracts the data for features and then optimized by the attention mechanism.ResultsThe model collects real-time carbon emissions data from sports events, including game times, lighting usage, air conditioning emissions and other carbon emissions data, and uses deep learning algorithms to predict and compare carbon emissions from sports competition.DiscussionIn identifying energy saving and emission reduction measures conducive to the realization of the goal of carbon neutral sports events, the model has a certain reference value for realizing energy saving and emission reduction in sports competitions under carbon neutrality goals.

【 授权许可】

Unknown   
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