Research on construction and management strategy of carbon neutral stadiums based on CNN-QRLSTM model combined with dynamic attention mechanism

Abstract

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IntroductionLarge-scale construction projects such as sports stadiums are known for their significant energy consumption and carbon emissions, raising concerns about sustainability. This study addresses the pressing issue of developing carbon-neutral stadiums by proposing an integrated approach that leverages advanced convolutional neural networks (CNN) and quasi-recurrent long short-term memory (QRLSTM) models, combined with dynamic attention mechanisms.MethodsThe proposed approach employs the CNN-QRLSTM model, which combines the strengths of CNN and QRLSTM to handle both image and sequential data. Additionally, dynamic attention mechanisms are integrated to adaptively adjust attention weights based on varying situations, enhancing the model's ability to capture relevant information accurately.ResultsExperiments were conducted using four datasets: EnergyPlus, ASHRAE, CBECS, and UCl. The results demonstrated the superiority of the proposed model compared to other advanced models, achieving the highest scores of 97.79% accuracy, recall rate, F1 score, and AUC.DiscussionThe integration of deep learning models and dynamic attention mechanisms in stadium construction and management offers a more scientific decision support system for stakeholders. This approach facilitates sustainable choices in carbon reduction and resource utilization, contributing to the development of carbon-neutral stadiums.

Keywords

• construction and management strategy
• carbon neutral stadiums
• CNN
• QRLSTM
• dynamic attention mechanism