一个基于序列对序列的新型深度学习模型,用于多步负载预测
IEEE transactions on neural networks and learning systems
|January 19, 2024
概括
这项研究引入了一种新的深度学习模型,用于多步负载预测,改进能源管理. 与时间序列分解的序列对序列模型相比,与现有方法相比,实现了更高的准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确的负载预测对于高效的电力系统运行至关重要,包括平衡供需,降低能源成本.
- 现有的方法往往侧重于单步预测,限制了它们在优化资源配置和决策方面的实用性.
- 多阶段负载预测为战略能源管理提供了增强的洞察力.
研究的目的:
- 提出一种新的深度学习模型,用于准确的多步负载预测.
- 在一个序列对序列框架内利用时间序列分解.
- 改进现有的负载预测技术,以实现更好的能源管理.
主要方法:
- 一个序列对序列 (Seq2Seq) 深度学习模型,包含一个时间序列分解策略.
- 该模型由残余连接的基本块组成,每块都有一个时间卷积网络 (TCN) 编码器和长短期内存 (LSTM) 解码器.
- 在每个基本区块内进行个别预测,最终结果汇总.
主要成果:
- 拟议的Seq2Seq模型在多步负载预测方面表现出卓越的准确性.
- 在多个真实世界的数据集上进行评估,该模型的性能优于几个基准预测方法.
- TCN-LSTM架构有效地捕获时间依赖性,以进行精确的预测.
结论:
- 具有时间序列分解的新型Seq2Seq深度学习模型在多步负载预测方面取得了重大进展.
- 这种方法为能源资源分配和电力系统的决策提供了更高的准确性和洞察力.
- 该模型的性能验证了其与既有预测技术的有效性.
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