基于人工智能模型的天然气价格预测
Xuhui Liu1, Meiqi Tang2, Yu Feng1
1School of Economics, Management and Law, Jilin Normal University, Siping, China.
PloS one
|December 1, 2025
概括
精确的多步预测天然气价格对于能源安全至关重要. 长短期记忆 (LSTM) 模型在预测价格波动方面表现出卓越的表现,在多天的预测中表现优于其他AI模型.
科学领域:
- 能源经济学 能源经济学
- 人工智能的人工智能
- 时间序列预测时间序列预测
背景情况:
- 俄罗斯-乌克兰冲突等地缘政治风险暴露了全球天然气供应链的脆弱性.
- 现有的人工智能驱动的能源价格预测往往缺乏多步预测分析,无法解决动态框架中的性能退化问题.
研究的目的:
- 构建和评估天然气价格的多步预测框架.
- 系统地比较四个人工智能模型在不同预测时间段内预测天然气价格方面的表现.
主要方法:
- 利用美国亨利中心 (1997-2024) 的每日天然气价格数据.
- 开发了一个多步预测框架,预测时间范围为1至4天.
- 比较了前神经网络,支向量机,随机森林和长短期记忆 (LSTM) 网络.
主要成果:
- 长短期记忆 (LSTM) 网络在所有预测步骤中始终呈现最低的错误率.
- 在一步预测中,LSTM模型实现了8.53%的平均绝对百分比误差 (MAPE).
结论:
- LSTM 模型为准确的多步骤天然气价格预测提供了强大的解决方案.
- 调查结果支持加强能源安全政策制定和优化能源市场交易策略.
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