融合Transformer、LSTM与CNN，提升汽油消费预测精度。
Advancing Gasoline Consumption Forecasting: A Novel Hybrid Model Integrating Transformers, LSTM, and CNN
- 三模型融合：利用Transformer捕捉长期依赖，LSTM处理时序记忆，CNN识别局部模式。
- 2007至2021年消费从6450万升/日升至9980万升/日，模型可预测至2031年。
- 适合能源政策制定者和环境规划人员参考，助力资源管理与减排决策。
伊朗拥有丰富的碳氢资源，在全球能源格局中占据重要地位。汽油作为关键燃料，支撑着该国交通运输体系。准确预测汽油消费对战略资源管理与环境规划至关重要。本文提出一种新型混合Transformer-LSTM-CNN模型，用于预测月度汽油消费。该模型结合了Transformer的自注意力机制、LSTM的时序记忆能力以及CNN的局部特征提取优势，相较于传统神经网络与回归模型，能更有效捕捉时间序列中的短长期依赖关系。基于Python实现，模型不仅提供未来消费预测，还通过温室气体排放分析评估环境影响。研究覆盖2007至2021年数据，期间日均消费从6450万升增至9980万升，并将预测延伸至2031年。结果表明，该混合模型显著提升了预测精度，凸显先进机器学习技术在优化能源管理与降低环境风险中的价值。
原文摘要 · Abstract (English)
Iran, endowed with abundant hydrocarbon resources, plays a crucial role in the global energy landscape. Gasoline, as a critical fuel, significantly supports the nation's transportation sector. Accurate forecasting of gasoline consumption is essential for strategic resource management and environmental planning. This research introduces a novel approach to predicting monthly gasoline consumption using a hybrid Transformer-LSTM-CNN model, which integrates the strengths of Transformer networks, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNN). This advanced architecture offers a superior alternative to conventional methods such as artificial neural networks and regression models by capturing both short- and long-term dependencies in time series data. By leveraging the self-attention mechanism of Transformers, the temporal memory of LSTMs, and the local pattern detection of CNNs, our hybrid model delivers improved prediction accuracy. Implemented using Python, the model provides precise future gasoline consumption forecasts and evaluates the environmental impact through the analysis of greenhouse gas emissions. This study examines gasoline consumption trends from 2007 to 2021, which rose from 64.5 million liters per day in 2007 to 99.80 million liters per day in 2021. Our proposed model forecasts consumption levels up to 2031, offering a valuable tool for policymakers and energy analysts. The results highlight the superiority of this hybrid model in improving the accuracy of gasoline consumption forecasts, reinforcing the need for advanced machine learning techniques to optimize resource management and mitigate environmental risks in the energy sector.
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