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多源数据对NGP预测有重要意义吗? 来自G-LSTM模型的证据
Jun Hao1,2, Shufan Shang1,2, Jiaxin Yuan1,2
1School of Economics and Management, University of Chinese Academy of Sciences, China.
Heliyon
|July 18, 2024
概括
准确预测天然气价格至关重要. 本研究介绍了一种使用多源数据和灰狼优化算法的混合模型,以提高能源市场决策的预测准确性.
科学领域:
- 能源经济学 能源经济学
- 计算智能是一种计算智能.
- 时间序列预测时间序列预测
背景情况:
- 天然气价格 (NGP) 对能源调度和规划至关重要.
- 由于固有的非线性和随机性,NGP预测具有挑战性.
- 现有的模型经常与影响因素的复杂性作斗争.
研究的目的:
- 为天然气价格开发一个多因素驱动的混合预测模型.
- 利用多源数据和先进的优化技术来改善预测.
- 加强能源调度,规划和控制方面的决策.
主要方法:
- 使用VADER和textstat.stat.从新闻文本中提取了情绪和可读性.
- 综合新闻和搜索索引数据,使用相关系数和CRITIC方法.
- 使用灰狼优化来微调长期短期记忆 (LSTM) 模型参数.
- 通过使用Henry Hub现货价格从2012-2022年验证了该模型.
主要成果:
- 多源数据显著提高了天然气价格的预测能力.
- 混合动力车型在各种场景中表现出卓越的性能.
- 废弃实验证实了结合多种数据源的价值.
结论:
- 拟议的混合模型为准确的NGP预测提供了一个有希望的方法.
- 整合从多源数据中获得的信息可以提高预测的有效性.
- 该模型为能源市场分析和决策支持提供了一个强大的工具.
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