相关实验视频
可解释的机器学习方法用于预测长距离原油管道中的电力消耗
Hanlin Chen1, Tao Gao2, Liang Wang2
1School of Architectural Engineering, Chongqing Industry Polytechnic University, Chongqing, 401120, China. chenhl@cqipu.edu.cn.
Scientific reports
|December 8, 2025
概括
这项研究引入了一种新的电网搜索-极端梯度提升 (GS-XGBoost) 模型,用于预测原油管道的电力消耗. 该模型实现了高精度,并提供可解释的结果,提高了能源效率和成本管理.
科学领域:
- 能源系统工程 能源系统工程
- 机器学习应用 机器学习应用
- 管道运输运输 管道运输
背景情况:
- 准确的电力消耗预测对于优化原油管道的能源使用和成本至关重要.
- 传统的机器学习模型在这个领域与非线性特征,准确性和可解释性作斗争.
研究的目的:
- 开发一种用于原油管道运输的新,准确和可解释的电力消耗预测模型.
- 解决传统机器学习算法在考虑影响因素和提取非线性特征方面的局限性.
主要方法:
- 集成网格搜索 (GS) 用于超参数优化和极端梯度提升 (XGBoost) 用于预测建模.
- 应用夏普利添加式解释 (SHAP) 来通过量化参数贡献来提高模型的解释性.
主要成果:
- 拟议的GS-XGBoost模型显著优于其他基准模型 (MLP,SVM,ELM,RF,GBRT).
- 获得了4.1%的平均绝对百分比误差 (MAPE) 和0.98.98的确定系数 (R2).
- SHAP分析确定了关键预测因素:每日运输量,出压力和车站压力/温度.
结论:
- 该GS-XGBoost模型为管道电力消耗提供了卓越的预测性能和可解释性.
- 这些发现支持提高能源效率,成本优化和管道运营的数字化转型.
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