基于DTW集群和物理约束时间GAN的空调负载数据生成方法
Yu Li1,2, Xiaoyu Yang2, Dongli Jia2
1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了一种新的混合框架,用于生成现实的空调负载数据,通过捕捉复杂的时间模式和物理约束来改进电网调度和能源管理.
科学领域:
- 能源系统工程 能源系统工程
- 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:
- 空调负载数据生成对于能源管理至关重要,但由于数据非静止性,复杂的影响因素和现有模型的局限性,它面临着挑战.
- 当前的模型经常产生平均分布,失去特定的时间模式,并且由于没有明确的约束,缺乏物理解释性.
研究的目的:
- 开发一个混合发电框架,准确地模拟各种时间模式,并确保空调负载数据的物理解释性.
- 通过整合聚类,物理约束的生成对抗网络和自适应模型选择来克服现有的数据驱动模型的局限性.
主要方法:
- 一个混合框架,将DTW集群用于数据分区,一个物理约束的TimeGAN (生成对抗网络) 与热力学一致性的内在温度负载相关性,以及基于LSTM (长短期内存) 的自适应子模型选择机制.
- DTW聚类细分数据以建模多样化的时间模式;无参数的物理约束确保在传感器稀缺的环境中保证热力学一致性;LSTM动态选择适应性时间融合的子模型.
主要成果:
- 拟议的框架在中国东南部的空调负载数据集上取得了0.98的本地相似度得分.
- 在生成准确和物理一致的负载数据方面,超越了最先进的模型11.4%和原来的TimeGAN13.3%.
结论:
- 混合框架有效地解决了空调负载数据生成中非静止性和缺乏物理解释性的挑战.
- 这种方法提高了产生的负载数据的准确性和可靠性,支持改进的电网调度和智能能源管理.
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