多阶段天然气负载预测,包括具有有限特征的数据复杂性分析.
Ning Tian1, Bilin Shao1, Huibin Zeng2
1School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究通过使用碎形维度和度来量化天然气负载的复杂性. 结合XGBoost,VMD和GRU的新型预测模型通过考虑这些复杂的特征,显著提高了预测准确性.
科学领域:
- 复杂系统分析 复杂系统分析
- 能源预测 能源预测
- 数据科学数据科学数据科学
背景情况:
- 数据的复杂性,包括自我相似性和长期内存,影响预测模型的准确性.
- 天然气负载数据表现出固有的复杂特征,这些特征挑战了传统的预测方法.
- 了解和量化这些特征对于开发强大的预测模型至关重要.
研究的目的:
- 量化和评估天然气负载数据的复杂性特征.
- 开发一个先进的多步预测模型,集成数据分解和集体深度学习.
- 将已识别的复杂性特征和气象因素纳入预测框架.
主要方法:
- 使用分数维度,赫斯特指数,样本和最大利亚普诺夫指数来量化复杂性.
- 使用 eXtreme Gradient Boosting (XGBoost) 的特征选和气象因素集成.
- 通过变化模式分解 (VMD) 和长期依赖模型与封闭的反复单位 (GRU) 进行数据分解.
主要成果:
- 与其他方法相比,拟议的XGBoost-VMD-GRU模型显示出优异的预测性能.
- 获得的高R平方值:0.9922 (1步),0.9860 (3步) 和0.9679 (6步).
- 综合复杂性特征显著提高了预测准确性和稳定性.
结论:
- 该研究成功地将数据复杂性分析集成到基于分解的预测框架中.
- 开发的模型为天然气负载预测提供了一种新且有效的方法.
- 这项研究为提高复杂系统预测的准确性和可靠性提供了创新的见解.
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