室内CO2的短期预测使用基于注意力的LSTM:希腊一家医院的用例
Christos Mountzouris1, Grigorios Protopsaltis1, John Gialelis1
1Department of Electrical and Computer Engineering, University of Patras, 265 04 Patras, Greece.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究使用先进的AI模型预测医院室内二氧化碳 (CO2) 水平. 一个基于注意力的LSTM模型准确预测了CO2度,改善了室内空气质量管理.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 卫生健康科学科学 医学
背景情况:
- 室内空气质量 (IAQ) 对健康,福祉和生产力至关重要.
- 二氧化碳 (CO2) 是一个重要的室内空气污染物和一个关键的IAQ指标.
- 精确的IAQ监测和预测是必不可少的,特别是在医院等敏感环境中.
研究的目的:
- 在医院环境中使用长短期记忆 (LSTM) 神经网络变体预测室内二氧化碳度.
- 评估不同LSTM架构的性能,以进行短期到中期的二氧化碳预测.
- 评估结合多变量数据 (气候条件,空气污染,占用) 对二氧化碳预测准确性的影响.
主要方法:
- 从希腊的一家医院收集传感空气污染和气候数据的数据.
- 实施和比较各种LSTM神经网络架构,包括基于注意力的变体.
- 预测室内二氧化碳度的时间范围从15分钟到180分钟.
- 使用平均绝对误差 (MAE) 评估模型性能.
主要成果:
- 基于注意力的LSTM在不同的预测时间段中表现一致.
- 一个基于多变量注意力的LSTM,结合了额外的预测因素,显著提高了预测准确性.
- 多变量模型的MAE值分别为8.9,16.7,31.2,38.9和39.5ppm,预测时间分别为15,30,60,120和180分钟.
结论:
- 基于注意力的LSTM模型对于预测室内二氧化碳度是有效的.
- 多变量数据集成进一步提高了医院环境中二氧化碳预测的准确性.
- 这些预测能力可以帮助主动的IAQ管理,并有助于更健康的室内空间.
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