首页|A novel deep learning framework with variational auto-encoder for indoor air quality prediction

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

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Exposure to poor indoor air conditions poses significant risks to human health,increasing morbidity and mortality rates.Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality.Based on partial least squares(PLS),we propose an indoor air quality prediction model that utilizes variational auto-encoder regression(VAER)algorithm.To reduce the negative effects of noise,latent variables in the original data are extracted by PLS in the first step.Then,the extracted variables are used as inputs to VAER,which improve the accuracy and robustness of the model.Through comparative analysis with traditional methods,we demonstrate the superior performance of our PLS-VAER model,which exhibits improved prediction performance and stability.The root mean square error(RMSE)of PLS-VAER is reduced by 14.71％,26.47％,and 12.50％compared to single VAER,PLS-SVR,and PLS-ANN,respectively.Additionally,the coefficient of determination(R2)of PLS-VAER improves by 13.70％,30.09％,and 11.25％compared to single VAER,PLS-SVR,and PLS-ANN,respectively.This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

Indoor air qualityPM2.5 concentrationVariational auto-encoderLatent variableSoft measurement modeling

Qiyue Wu、Yun Geng、Xinyuan Wang、Dongsheng Wang、ChangKyoo Yoo、Hongbin Liu

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Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources,Nanjing Forestry University,Nanjing 210037,China

College of Automation & College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

Department of Environmental Science and Engineering,College of Engineering,Kyung Hee University,Yongin 446701,Republic of Korea

Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control,College of Light Industry and Food Engineering,Guangxi University,Nanning 530004,China

Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province,Shandong Huatai Paper Co.Ltd.,Dongying 257335,China

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Opening Project of Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control,China山东省自然科学基金国家自然科学基金Natural Science Foundation of Jiangsu Provincial Universities,China

2021KF11ZR2021MF1355217000122KJA530003

2024

10.1007/s11783-024-1768-7

环境科学与工程前沿
高等教育出版社

环境科学与工程前沿

影响因子：0.545
ISSN：2095-2201
年,卷(期)：2024.18(1)
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