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SmartBuildSim:一个开源的合成双胞胎框架,用于智能建筑分析中的可重复AI基准测试
Tymoteusz Miller1,2, Irmina Durlik3, Agnieszka Nowy3
1Institute of Marine and Environmental Sciences, University of Szczecin, 70-383 Szczecin, Poland.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
智能BuildSim是一个开源框架,用于人工智能研究生成现实的建筑数据. 它提供可重复的,可配置的合成传感器流,使预测和异常检测模型的可靠测试成为可能.
科学领域:
- 建筑能源系统 建筑能源系统
- 智能建筑中的人工智能
- 数据科学和模拟数据科学
背景情况:
- 高保真建筑模拟器在计算上昂贵.
- 现有的合成数据集往往缺乏现实的时间动态和可重现性.
- 智能建筑中的AI研究需要可靠和可扩展的测试台.
研究的目的:
- 介绍SmartBuildSim,一个开源的合成双胞胎框架.
- 允许生成可配置和可重复的多传感器建筑数据流.
- 为人工智能研究提供一个轻量级的替代复杂的物理数字双胞胎.
主要方法:
- 使用了具有可调节参数 (趋势,季节性,相关性,延迟,异常) 的轻量级统计模型.
- 实施了确定性播种,以实现实验级别的可重复性.
- 开发了模块化管道,用于跨预测,异常检测和强化学习 (RL) 的统一评估.
主要成果:
- 与ASHRAE参考信号相比,经过验证的合成数据显示了现实的大小和变化 (KS ≈ 0.32; DTW ≈ 9.69).
- 通过线性模型 (RMSE ≈ 21.27) 展示了强大的预测性能.
- 展示了卓越的异常检测 (隔离森林与LOF:F1 ≈ 0.17 与0.10) 和RL融合 (软Q学习差异减少了>95%).
结论:
- 智能BuildSim提供了一个透明,轻量级和可重复的替代高保真模拟器.
- 该框架有效地弥合了简单的合成数据和复杂的物理数字双胞胎之间的差距.
- 为推进智能建筑中的AI研究提供了一个实用的测试平台.
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