Data Analytics for Home Air Quality Monitoring
Modern air quality monitoring systems are characterised by high complexity and costs. The expensive embedded units such as sensor arrays, processors, power blocks, displays and communication units make them less appropriate for small indoor spaces.
In this paper we demonstrate that two widely available, in private houses, sensors (for Humidity and Temperature) are promising alternative, to the expensive indoor air quality solutions, provided with intelligent data processing tools. Our findings suggest that neural network based data analytics system can learn to discriminate unusual indoor gases from normal home air components based only on temperature and humidity measurements.
KeywordsIndoor air quality Data analytics Neural network Deep Autoencoder Neural Network
This study has been done during the traineeship program of PhD student Petya Mihaylova in University of Aveiro funded by ERASMUS+EU programme for education, training, youth and sport, supported by technical University of Sofia, Bulgaria.
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