来自办公室,礼堂和医院建筑的空气处理装置的实际操作标记数据
1Institute for Environmental Design and Engineering, University College London, London, WC1H 0NN, UK. seung-hyun.wang@ucl.ac.uk.
Scientific data
|August 25, 2025
概括
这项研究为具有恒定空气量 (CAV) 系统的空气处理装置 (AHU) 创建了运行数据集,以改进自动故障检测和诊断 (AFDD). 这些数据集有助于开发和整合建筑能效的先进诊断模型.
科学领域:
- 建筑能源系统
- 高温空调性能分析
- 用于设施管理的数据科学
背景情况:
- 空气处理装置 (AHU) 对于建筑气候控制至关重要.
- 恒定空气容量 (CAV) 系统需要有效的故障检测才能达到最佳性能.
- 现有的数据集往往缺乏用于自动故障检测和诊断 (AFDD) 模型开发的真实操作数据.
研究的目的:
- 为具有CAV系统的AHU生成全面的,现实世界的操作数据集.
- 支持机器学习和AFDD诊断模型的发展.
- 促进已验证的AFDD模型融入设施管理系统.
主要方法:
- 根据办公室,礼堂和医院建筑的独特运营特征进行数据收集.
- 严格清理数据,包括识别和删除缺失或重复的数据点.
- 系统注释操作条件和故障类别.
- 数据集的战略分区为培训,验证和测试子集.
主要成果:
- 为三个建筑类型开发不同的高质量的操作数据集.
- 有操作状态和故障信息的数据集.
- 准备用于培训和验证AFDD模型的数据.
结论:
- 创建的数据集对于完善和推进AHU的AFDD模型至关重要.
- 这些数据集的可用性将加速自动故障检测解决方案的开发和部署.
- 增强的AFDD能力将提高建筑性能,可靠性和能源效率.
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