基于Informer模型的住宅室内PM<sub>2.5</sub>浓度预测与模型优化研究

孙文; 阎卫东; 黄凯良; 宋嘉森

doi:10.13205/j.hjgc.202512018

基于Informer模型的住宅室内PM_2.5浓度预测与模型优化研究

doi: 10.13205/j.hjgc.202512018

1. 沈阳建筑大学市政与环境工程学院, 沈阳 110168;
2. 沈阳建筑大学土木工程学院, 沈阳 110168;
3. 重庆大学土木工程学院, 重庆 400044

基金项目:

国家自然科学基金项目“严寒地区住宅厨房排烟耦合过程机理与热环境改善方法研究”（52178082）

详细信息

作者简介:
孙文（1993—），女，博士研究生，主要研究方向为居住建筑室内空气品质。sunwengoal@163.com

通讯作者:
黄凯良（1985—），男，教授，主要研究方向为室内空气质量保障技术、建筑通风节能等。huangkailiang_v@163.com

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出版历程
- 收稿日期: 2024-12-05
- 录用日期: 2025-01-28
- 修回日期: 2025-01-10
- 网络出版日期: 2026-01-09

Prediction of residential indoor PM_2.5 concentration and model optimization based on Informer model

1. School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
2. School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
3. School of Civil Engineering, Chongqing University, Chongqing 400044, China

摘要

摘要: 在严寒地区供暖季期间，住宅室内常存在较高浓度的PM_2.5污染，准确预测室内PM_2.5污染水平对于制定有效的净化措施至关重要。针对严寒地区7户住宅在供暖季期间的室内PM_2.5浓度及居住者的开窗行为进行了分析，得出住宅客厅的PM_2.5浓度超标率可达35.94%，室内日平均开窗持续时长较短。同时利用室内PM_2.5历史浓度数据对Informer模型进行训练，将住宅开窗时长作为特征值输入，实现对室内PM_2.5浓度的预测。通过对比不同特征输入策略、输入步长、预测范围下和加入滚动预测后模型的预测表现，综合评估后确定了模型实现高精度预测的最佳配置。该模型能够预测未来一段时间内的室内PM_2.5浓度，其预测性能的评价指标平均绝对误差（MAE）为10.70 μg/m³，均方根误差（RMSE）为13.75 μg/m³及可决系数R²为0.795。与TCN-LSTM模型相比，Informer模型的R²提高了13.9%。优化后的模型呈现了更加精确的预测效果。
- 室内PM_2.5污染 /
- PM_2.5浓度预测 /
- Informer模型 /
- 模型优化
Abstract: During the heating season in severe cold region， there is often a high concentration of PM_2.5 pollution indoors. Accurately predicting indoor PM_2.5 pollution levels is crucial for developing effective purification measures. The indoor PM_2.5 concentrations and window-opening behaviors of 7 residential buildings in a severe cold region during the heating season were analyzed. The PM_2.5 concentration exceeded the standard level by 35.94% in the living room， and the daily average duration of window-opening was relatively short. At the same time， the Informer model was trained using indoor PM_2.5 concentration historical data， with the duration of window-opening as a feature input to achieve the prediction of indoor PM_2.5 concentration. By comparing the prediction performance of the model under different feature input strategies， input step sizes， prediction ranges， and adding rolling prediction， the optimal configuration of the model to achieve high-precision prediction was determined after comprehensive evaluation. The model was capable of predicting indoor PM_2.5 concentrations for a future period， and the evaluation metrics for its prediction performance were： MAE of 10.70 μg/m³， RMSE of 13.75 μg/m³， and R² of 0.795. Compared to the TCN-LSTM model， the R² of the Informer model increased by 13.9%. The optimized model presents a more accurate prediction effect.
- indoor PM_2.5 pollution /
- PM_{2. 5} concentration prediction /
- Informer model /
- model optimization

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参考文献(16)

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