一种基于贝叶斯网络的混合深度学习方法,将气候和可靠性因素结合起来,以预测电动汽车的充电能力
1Information Technology and Management Program, Ming Chuan University, Taoyuan City, 33321, Taiwan, ROC.
Heliyon
|March 5, 2025
概括
准确的电动汽车 (EV) 充电需求预测至关重要. 本研究介绍了一种混合模型,将队列,贝叶斯网络和深度学习结合起来,以提高预测准确性和优化电动汽车基础设施可靠性.
科学领域:
- * 电气工程 电气工程
- * 计算机科学 计算机科学
- * 运营研究 运营研究
背景情况:
- * 越来越多的电动汽车 (EV) 需要复杂的模型来预测充电需求.
- *可靠的电动汽车基础设施规划取决于精确预测能源需求.
- *现有的模型往往缺乏整合各种影响因素的能力.
研究的目的:
- * 开发一种新的分析框架,以提高电动汽车充电需求预测的准确性.
- *通过考虑气象条件和充电故障,创建一个全面的系统,优化电动汽车基础设施.
- *通过先进的预测建模,提高电动汽车充电基础设施的可靠性.
主要方法:
- *开发基于贝叶斯网络的混合深度学习 (HBNDL) 系统架构.
- * 用深度学习技术集成队列网络和贝叶斯网络模型.
- *利用广泛的交易数据和气候分析来建模充电的可靠性.
- *设计算法来评估充电站的使用和可靠性.
主要成果:
- * HBNDL框架显著提高了电动汽车充电需求预测的准确性.
- * 拟议的模型证明了电动汽车充电基础设施的可靠性提高.
- *实验结果显示,与现有模型相比,在短期,中期和长期预测方面表现优越.
- * 该框架有效地考虑了对收费需求的环境影响.
结论:
- * 集成的HBNDL框架为预测电动汽车充电需求提供了强大而可适应的解决方案.
- *这种方法优化了电动汽车基础设施的规划和管理.
- *这项研究验证了将队列理论,贝叶斯网络和深度学习结合起来,用于复杂的预测任务的有效性.
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