通过机器学习和整体方法优化电动汽车能源消耗预测
Izhar Hussain1,2, Kok Boon Ching3, Chessda Uttraphan4
1Departement of Electrical Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johar, 86400, Malaysia.
Scientific reports
|August 8, 2025
概括
预测电动汽车的能源消耗是至关重要的. 使用组合模型和时间特征的新型机器学习方法显著提高了可持续能源管理的预测准确性.
科学领域:
- 能源系统 能源系统
- 机器学习 机器学习
- 运输工程 运输工程
背景情况:
- 准确的电动汽车 (EV) 能源消耗预测对于效率和基础设施规划至关重要.
- 由于驾驶条件,车辆规格和环境之间的复杂相互作用,存在挑战.
- 现实世界的数据分析是理解和优化电动汽车能源使用的关键.
研究的目的:
- 开发和验证数据驱动的机器学习方法,用于预测电动汽车的能源消耗.
- 系统地比较K-最近邻居 (KNN) 回归的超参数优化方法.
- 创建和评估一个堆叠混合组合模型,以提高预测准确度.
主要方法:
- 利用来自科罗拉多州的大量现实世界数据集进行分析.
- 采用K-Nearest Neighbors (KNN) 作为基模型,通过GridSearchCV,RandomizedSearchCV,Optuna和粒子优化 (PSO) 进行超参数优化.
- 开发了一个堆叠混合组合模型,将KNN与基于树的模型结合起来,并结合了新的时间特征工程.
主要成果:
- 堆叠混合组合模型表现出卓越的性能,实现了最低的预测误差 (MAE = 0.645880,RMSE = 1.788540) 和最高的准确性 (R2 = 0.960078).
- 奥普图纳被确定为KNN模型中最有效的超参数优化技术.
- 时间特征提取和优化组合建模显著提高了预测准确性.
结论:
- 集体学习和先进的优化方法对于改善电动汽车能源消耗预测非常有效.
- 开发的模型为电动汽车制造商和政策制定者提供了可部署的可持续能源管理工具.
- 优化集体建模与时间特征提供了一个强大的解决方案,用于准确的电动汽车能源使用预测.
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