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预测土耳其两个城市的每月住宅天然气需求,使用即时学习建模

Burak Alakent¹, Erkan Isikli², Cigdem Kadaifci²

¹Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.

|June 11, 2025

概括

由于土耳其依赖进口,准确的天然气需求预测至关重要. 一种新的机器学习方法Just-in-Time-Learning-Gaussian Process Regression (JITL-GPR) 与传统模型相比显示出较少的预测误差.

科学领域:

能源经济学能源经济学
时间序列分析时间序列分析
机器学习机器学习

背景情况:

全球天然气 (NG) 消费量正在上升,土耳其严重依赖进口.
准确预测每月的天然气需求 (NGD) 对土耳其的进口合同至关重要.
现有的时间序列模型可能无法完全捕捉复杂的需求模式.

研究的目的:

预测土耳其1年9个月的月度NGD.
对传统的时间序列模型进行新型机器学习方法的评估.
提高NGD预测对能源进口计划的准确性.

主要方法:

利用了土耳其布尔萨和凯塞里的历史月度NG消费数据 (2014-2024年).
应用了传统的时间序列模型,如SARIMA和ETS.
开发并测试了一种新的即时学习高斯过程回归 (JITL-GPR) 方法,使用二维特征表示.

主要成果:

与SARIMA和ETS模型相比,JITL-GPR方法表明预测错误减少.
在JITL-GPR中的新型特征表示提高了预测准确度.
JITL-GPR在样本外的NGD月度预测中被证明是有效的.

结论:

JITL-GPR是准确预测土耳其天然气需求的一个有前途的工具.
该方法提供了易于使用和优化.
减少预测错误有助于更好地管理能源进口合同.

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