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As global climate change intensifies, carbon neutrality has become an urgent goal for societies worldwide. Population growth, urbanization, and demographic shifts play a crucial role in shaping energy consumption patterns and carbon emissions. To mitigate climate change and reduce greenhouse gas emissions, various industries are actively seeking innovative solutions to minimize their environmental impact.
Deep learning, a critical branch of artificial intelligence, has made breakthrough achievements in image recognition, natural language processing, and speech recognition by mimicking the human nervous system’s learning process from large datasets (Lyu et al., 2024; Shi et al., 2022; Xi et al., 2024). In the realm of environmental protection and sustainable development, deep learning is widely applied in energy management, climate simulation, and intelligent transportation, providing powerful tools and methodologies to address complex environmental challenges.
The sports industry involves significant energy consumption and carbon emissions during the operation of events, activities, and facilities. Therefore, optimizing energy usage and reducing carbon footprints are crucial in the carbon neutrality process (Karpov et al., 2019). In recent years, deep learning technology has been gradually introduced into the sports sector. Image recognition has been used to monitor and analyze sports venues and facilities, optimizing energy usage and reducing waste. Intelligent sensors, combined with deep learning algorithms, monitor energy consumption in real time during events and training, assisting managers in making more environmentally friendly decisions (Smagulova & James, 2019).
Additionally, deep learning applications in data analysis can predict the energy demands of events and activities, enabling more efficient resource allocation (Zhao & Li, 2023). For example, recurrent neural networks and their improved versions, such as long short-term memory networks and gated recurrent units, perform exceptionally well in handling time-series data by capturing temporal dependencies. However, these models face efficiency issues when processing long sequences of data and are sensitive to hyperparameter adjustments (Lin et al., 2022).
Convolutional neural networks (CNNs), on the other hand, are particularly advantageous image-processing networks for extracting local spatial features, excelling in venue and area energy analysis. Nevertheless, they are limited when handling time-series data (Bhatt et al., 2021; Fan et al., 2023).
Recently, transformer models, which have demonstrated outstanding performance in natural language processing, have been introduced to energy consumption prediction tasks due to their self-attention mechanisms (Li et al., 2022). Their multi-head attention mechanism effectively captures global features and offers high parallel processing capabilities. However, the complexity of transformer models results in high computational costs, and their interpretability remains a challenge (Acheampong et al., 2021; Pang et al., 2024). Despite the successes of these models in energy management, balancing prediction accuracy, computational efficiency, and model complexity remains a critical research direction.
Although deep learning technologies have shown great potential in carbon neutrality practices, existing research still faces several challenges, particularly in feature extraction, model design complexity, and prediction accuracy. Traditional methods, for example, struggle to accurately capture key features and behavioral patterns when processing complex carbon emission data. Moreover, variations in population density, migration trends, and regional demographic structures further complicate energy consumption modeling, requiring more adaptable and dynamic prediction frameworks. Furthermore, addressing data quality issues and improving the adaptability of models across different scenarios remain pressing gaps in research.