LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors

LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors

Monitoring individual exposure to indoor air pollutants is crucial for human health and well-being. Due to the high spatiotemporal variations of indoor air pollutants, ubiquitous sensing is essential. However, the cost and maintenance associated with physical sensors make this currently infeasible....

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Bibliographic Details
Main Authors: Martin Gabriel, Thomas Auer
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Buildings
Subjects:
machine learning
deep learning
virtual sensing
LSTM
IAQ
monitoring
Online Access:https://www.mdpi.com/2075-5309/13/7/1684

Internet

https://www.mdpi.com/2075-5309/13/7/1684

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