使用多变量输入和变量模式分解与模式选择进行船舶电力需求的增强预测
Paolo Fazzini1, Giuseppe La Tona2, Matteo Diez3
1Institute of Marine Engineering (INM), National Research Council (CNR), Palermo, Italy.
Scientific reports
|July 4, 2025
概括
准确的船舶电力需求预测是通过一种新的混合方法实现的. 这种方法结合了变化模式分解与模式选择 (VMDMS) 和长短期内存 (LSTM) 网络,以改进能源管理系统 (EMS).
科学领域:
- 海洋工程 海洋工程
- 电气工程 电气工程
- 数据科学数据科学数据科学
背景情况:
- 优化船上能源管理系统 (EMS) 需要准确的电力需求预测.
- 有效的船舶运营和利能力取决于电网的有效EMS.
研究的目的:
- 为船舶电力需求引入一种新的混合预测方法.
- 提高能源管理的多变量时间序列预测的准确性.
主要方法:
- 一种新的变量模式分解与模式选择 (VMDMS) 技术被开发用于多变量时间序列分解.
- 长短期记忆 (LSTM) 网络被用于使用分解的时间序列进行预测.
- 混合VMDMS-LSTM模型在现实世界船舶电力需求数据上得到验证.
主要成果:
- VMDMS方法有效地识别了跨道的协同模式,提高了预测准确度.
- 混合式方法在预测船上电力需求方面表现优异.
- 该方法将变化模式分解 (VMD) 的适用性扩展到无限制数据假设的多变量预测.
结论:
- 拟议的混合预测方法显著提高了船上电力需求预测的准确性.
- 这项研究推进了用于海洋能源管理中的预测建模的分解技术.
- 这些发现为提高船上电网效率提供了新的途径.
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