CLM-former用于增强智能微电网中的多地平线时间序列预测和负载预测,使用强大的基于变压器的模型
S Mozhgan Rahmatinia1, Seyed-Majid Hosseini1, Seyed-Amin Hosseini-Seno2
1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Scientific reports
|January 28, 2026
概括
通过结合时间序列分解和新的注意力机制,CLM-Former改善了住宅负载预测. 这种混合深度学习模型准确预测电力使用量,提高智能电网效率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的多地平线负载预测对于智能电网的稳定性和效率至关重要.
- 像Autoformer这样的变压器模型捕捉周期性,但与现实数据的快速变化作斗争.
- 住宅用电消费表现出复杂的长期趋势和短期波动.
研究的目的:
- 开发一种新的深度学习架构,用于增强住宅负载预测.
- 提高多地平线电力消耗预测的准确性.
- 解决现有模型在捕捉局部化和动态模式方面的局限性.
主要方法:
- 提出了CLM-Former,这是一个混合深度学习架构.
- 集成的时间序列分解,基于自相关的注意力和卷积循环子网络 (CLM-subNet).
- 对真实世界智能电表数据的评估性能与基线模型相比.
主要成果:
- 在多个预测时间段中,CLM-Former表现出强大而适应性强的性能.
- 该模型有效地捕捉了季节性依赖和高分辨率的电力使用变化.
- 在综合评估中表现优于各种基于变压器和深度学习的基线.
结论:
- CLM-Former是住宅能源预测的一个有前途的工具.
- 混合架构成功模拟了长期周期趋势和短期动态.
- 这些发现对需求响应和智能电网管理有重大影响.
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